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\title{QLDNet: A Lightweight and Efficient Network for High-Robustness Aerial Human Detection in UAV-Based Remote Sensing%\thanks{Grants or other notes
%about the article that should go on the front page should be
%placed here. General acknowledgments should be placed at the end of the article.}
}
%\subtitle{Do you have a subtitle?\\ If so, write it here}

%\titlerunning{Short form of title}        % if too long for running head

\author{Mandun Zhang\textsuperscript{1,2,3}\and
Jiale Shi\textsuperscript{1}\and
Chenyue Hou\textsuperscript{1}\and
Yonghui Pang\textsuperscript{1}\and
Yiqi Zhang\textsuperscript{1}\and
Xiangsheng Huang\textsuperscript{4,*}}

%\authorrunning{Short form of author list} % if too long for running head


\date{Received: 15 February 2025 / Accepted: }
% The correct dates will be entered by the editor
\institute{Mandun Zhang \at
              \email{zhangmandun@scse.hebut.edu.cn}           %  \\
%             \emph{Present address:} of F. Author  %  if needed
           \and
           Jiale Shi \at
              \email{185721@stu.hebut.edu.cn}
           \and
           Chenyue Hou \at
              \email{202332803008@stu.hebut.edu.cn}
           \and
           Yonghui Pang \at
              \email{202332803003@stu.hebut.edu.cn}
           \and
           Yiqi Zhang \at
              \email{1751816762@qq.com}
           \and
           Xiangsheng Huang \at
              \email{huangxiangsheng@xii.ac.cn}
            \and
        1 School of Artificial Intelligence,Hebei University of\\ Technology,5340 Xiping Road,Tianjin,300401,Tianjin,China.\at
            \and
        2 School of Information Science and Engineering, Xinjiang \\
        College of Science and Technology,No.360,Jinhe Road,Korla \\
        Economic and Technological Development Zone, Bayingolin\\
        Mongol Autonomous Prefecture,841000,Xinjiang,China.\at
            \and
        3 Tianjin International Joint Center for Virtual Reality\\
        and Visual Computing, 5340 Xiping Road,Beichen \\
        District,Tianjin,300401,Tianjin,China.\at
            \and
        4 Hebei Key Laboratory of Cognitive Intelligence,Xiong’an\\
        Institute of Innovation,Xiong’an New Area,071700,Hebei,\\
        China.
}
\maketitle

\columnbreak

\begin{abstract}
Accurate detection of extremely small targets in aerial high-resolution images is critical for military and civilian applications. However, challenges such as small target size, complex backgrounds, and target deformation hinder optimal performance. We propose a lightweight network that addresses these issues through a synergistic combination of non-strided convolution and decoupled large-kernel convolutional attention. Specifically, the non-strided convolution constructs a quadruple aggregate and connection feature extractor (QACFE) module, which maps features into the channel dimension while preserving spatial details. The decoupled large kernel convolutional attention(DLKA) then leverages these channel features to effectively extract structural and edge-related low frequency information, while simultaneously reducing computational costs and model size caused by the increased channel dimensionality after QACFE. A learnable offset mechanism is introduced, transforming the detection head into a deformable detection head(DDH). Additionally, the network incorporates a PAFPN (Path Aggregation Feature Pyramid Network) structure to efficiently extract multi-scale features. During inference, a tailored approach performs pixel-level multi-scale detection. This enhances small target detection by merging features and pixels through dual-scale fusion, integrating multi-scale feature extraction with multilevel feature integration.The lightweight network is QLDNet,a dual multi-scale network based on QACFE, DLKA and DDH.
Experimental results demonstrate that QLDNet achieves efficient and accurate small target detection, with an accuracy rate as high as 94.0\% and an inference time of 0.12 seconds per image, meeting the system's real-time requirements with fewer parameters and satisfying processing speeds.The code can be find at https://github.com/Sjl185721/QLDNet-v1.git.
\keywords{Object Detection\and Rescue\and Drone perspective\and Computer vision\and Remote Sensing}
% \PACS{PACS code1 \and PACS code2 \and more}
\subclass{MSC 68T05 \and MSC 86Axx \and MSC 68T10 }
\end{abstract}

\section{Introduction}
\label{intro}
The study of target detection algorithms for low altitude drone aerial imagery holds significant practical value, particularly in the critical domain of post-disaster victim search and rescue. Drones, with their flexibility and efficiency, are playing an increasingly essential role in disaster response efforts. Equipped with high-resolution cameras, drones can rapidly survey disaster-affected areas, conducting large-scale aerial imaging to provide rescue teams with crucial information. Research on target detection algorithms, especially those based on deep learning, can substantially enhance the ability of drones to identify victims in complex environments.\cite{jiang2021survey}

The objective of this research is to develop algorithms capable of accurately and efficiently detecting human targets in post-disaster rubble images. This capability is critical for large-scale, high-efficiency search and victim counting operations. However, the lack of realistic datasets featuring post-disaster rubble imagery often results in suboptimal performance in practical applications. For instance, Pasi et al. developed the HERIDAL dataset specifically for human detection in post-disaster scenarios. By comparing detection performance across different datasets and varying training proportions, they demonstrated the effectiveness and superiority of HERIDAL. Additionally, they evaluated and benchmarked 11 state-of-the-art algorithms on HERIDAL, providing valuable insights and references for drone-based post-disaster victim search efforts\cite{pyrro2022}.

\begin{figure} % picture
    \centering
    \includegraphics[width=0.48\textwidth]{fig4.png}
    \caption{Difficulty of Locating Person}
    \label{figure:fig4}
\end{figure}
Research on drone-based target detection algorithms not only improves the efficiency of search-and-rescue operations and reduces response time but also enhances the accuracy and safety of rescue efforts in the complex and dynamic environments of disaster-stricken areas. Consequently, the study of target detection algorithms for low-altitude drone aerial imagery is extremely important for increasing the efficiency and precision of post-disaster rescue missions and represents a key technology in the application of drone systems\cite{dousai2022}.However,as is shown in Figure \ref{figure:fig4}, the challenges inherent in low-altitude drone imagery, such as significant variations in target size, a high prevalence of small targets, and the tendency of targets to blend with complex backgrounds, make it difficult for current detection algorithms to achieve high precision. Moreover, targets captured from a drone's top-down perspective often exhibit complex pose variations, and existing algorithms struggle with issues of robustness when handling target deformation and occlusion. These limitations significantly constrain the practical performance of low-altitude drone target detection algorithms.

\section{Related works}
\label{Related_works}
\subsection{Local detectors based on handcrafted feature extraction}
Local detectors based on handcrafted feature extraction refer to methods in computer vision that rely on predefined, manually designed features to detect specific objects or patterns in images. These features are crafted by researchers according to the characteristics of the visual task, such as edges\cite{lowe2004}, corners\cite{dalal2005}, textures, or colors. Researchers select the most discriminative features from the extracted set to distinguish between different categories or objects. These features are then used to train classifiers such as Support Vector Machines (SVMs)\cite{chandra2021}, decision trees\cite{breiman1984}, or random forests\cite{breiman2001}.

During the testing process, a sliding window approach is typically employed, where features are extracted at every position in the image and classified using the trained classifier to determine whether a target object is present. For example, Felzenszwalb et al. proposed the Deformable Part Model (DPM) algorithm\cite{felzenszwalb2009}, which addresses the multi-view problem of objects by adopting a multi-component strategy. This involves using part filters and root filters to extract local and global information, respectively. The training phase requires a dataset annotated with the positions of both the objects and their parts to train the DPM model.

However, these methods have significant limitations. They lack generalizability for diverse detection tasks, as they rely on complex prior knowledge\cite{bay2008} and require domain experts to design features manually. This makes automation challenging. Compared to deep learning-based approaches, handcrafted feature extractors often exhibit inferior performance in complex real-world scenarios\cite{krizhevsky2012}.

\subsection{End-to-End Detectors Based on Deep Learning}
With the advancement  of deep learning, detectors based on deep features\cite{paul2023utilizing}(e.g., the R-CNN series\cite{xie2021oriented}, YOLO\cite{terven2023comprehensive}, SSD\cite{liu2016ssd}, etc.) have become mainstream. These methods can automatically learn more complex and abstract feature representations, generally achieving superior performance. Deep learning-based visual detection and recognition methods for drones can be categorized into single-stage and two-stage detectors\cite{zhu2020}.

Single-stage detection algorithms significantly improve computational speed by formulating the object detection problem as a regression problem. These algorithms typically use neural networks to extract high-level features from data and fuse feature maps to achieve object localization. Common single-stage detection algorithms include YOLO, SSD, and RetinaNet, with their workflow illustrated in Figure \ref{figure:fig1}.
\begin{figure}[htbp] % picture
    \centering
    \includegraphics[width=0.5\textwidth]{fig1.png}
    \caption{Single-stage detection algorithm}
    \label{figure:fig1}
\end{figure}

Due to the lightweight and fast characeristics of single-stage detection, this study selects the YOLO framework as the research foundation. The Backbone, based on CSPNet, generates multi-scale feature maps to handle objects of different sizes. The Neck network fuses the multi-scale feature maps extracted by the Backbone, enhancing the adaptability of object detection to varying target sizes. The processed multi-scale feature maps from the Neck are then passed to the Head for classification and regression, ultimately producing detection boxes.

Two-stage detection algorithms involve two sequential steps for object detection. First, a region proposal module identifies candidate regions by distinguishing between foreground and background areas. Next, a candidate region classification and coordinate refinement module serves as the second detector, classifying objects and performing position regression to determine the accurate category and location of targets\cite{liu2020}. The workflow of two-stage detection is illustrated in Figure \ref{figure:fig2}.

Compared to single-stage detection algorithms, two-stage models are more complex but achieve higher detection accuracy. Representative methods include Faster R-CNN and Mask R-CNN.
\begin{figure} % picture
    \centering
    \includegraphics[width=0.48\textwidth]{fig2.png}
    \caption{Two-stage detection algorithm}
    \label{figure:fig2}
\end{figure}
\subsection{Detectors for Tiny Objects}

The HERIDAL dataset utilized in this study\cite{pyrro2022} contains target bounding boxes that occupy only 0.02\% of the entire image area, posing a significant challenge for the detection of tiny objects. The small proportion of pixels representing the objects in the image results in insufficient detail, which impeding detection accuracy. Efforts to address the difficulties in small-object detection can be categorized into four main approaches: data augmentation, multi-scale learning, context learning, and generative adversarial learning. Among these, multi-scale learning has been the most comprehensively studied. Tiny objects require both deep semantic information and shallow representational information, and multi-scale learning combines these two types of information, making it an effective strategy for improving small-object detection performance.

Current methods for handling multiscale issues can be summarised into two main categories: image pyramid based multiscale object detection and feature pyramid based multi-scale object detection\cite{deng2018}. In deep learning-based object detection tasks, feature pyramid-based methods are more widely explored. Feature pyramid-based multi-scale object detection encompasses two approaches: combining multiple feature maps to create a composite feature map for object detection, as utilized in ION\cite{bell2015} and HyperNet\cite{kong2016}; and direct detection on multiple feature maps by aggregating results from various convolutional layers, as seen in SSD and FPN-based Tamos\cite{mayer2024} methods.

However, models trained in these ways typically have high computational demands and are not suitable for resource-constrained edge devices, and the inference process is time-consuming, as shown in Figure \ref{figure:fig3}.

\begin{figure} % picture
    \centering
    \includegraphics[width=0.48\textwidth]{fig3.png}
    \caption{Limited Inference Speed for Edge Computing}
    \label{figure:fig3}
\end{figure}

\subsection{Progress in Deep Learning-Based Object Detection Algorithms for Unmanned Aerial Vehicle (UAV) Aerial Images}
Current research on aerial target detection using drones focuses on lightweight design, multi-scale feature enhancement, and multimodal fusion to address core challenges such as edge deployment efficiency, missed detection of small targets, and robustness in complex environments.

Two-stage object detection algorithms, such as the Multi-Depth Image Pyramid Network (MD-IPN), use multiple depth backbones to maintain multiple positional information, extracting various features from high to low resolution, thereby addressing the issue of context information loss for small targets while maintaining detection performance for medium to large targets\cite{tian2024multi}. The Multi-Scale Feature Pyramid Network (MS-FPN) can restore missing features in deep layers through parallel branches and employs a Supervised Spatial Attention Module to weaken the impact of background noise inference and focus on target information\cite{van2023feature}. HRDNet maintains multiple positional information using multiple depth backbones, with high-resolution inputs to shallow networks and low-resolution inputs to deep networks\cite{liu2021hrdnet}. Cascade ResNet50 introduces DeForm convolutional layers in the backbone, integrates features of different scales, and extracts regions of interest using RPN, improving adaptability to viewpoint changes, albeit with lower network efficiency\cite{yang2019effective}.

A one-stage algorithm, such as Aerial person detection (APD), a variant of YOLOv5, employs a deep teacher model to generate pseudo-labels that guide the training of a shallow student model. Combined with a random weight initialization strategy, it achieves model lightweighting while ensuring detection accuracy\cite{zhang2024robust}. FS-SSD utilizes two feature pyramids generated by a deconvolution module and a feature fusion module for joint detection\cite{liang2020small}. SPB-YOLO integrates the Bottleneck architecture with the Strip Module to design the SPB module, which is embedded into the YOLOv5 network, and an additional detection head is added to achieve dense detection\cite{wang2021spb}.

Despite the progress made, most methods either prioritize accuracy or speed, failing to address both simultaneously in resource-constrained settings. Techniques such as image pyramids\cite{deng2018multi} improve multi-scale detection but incur high computational costs. Our QLDNet bridges these gaps by integrating dual multi-scale detection (at the feature and pixel levels), non-striding convolutions with channel aggregation, and a deformable detection head. This design maintains computational efficiency while preserving fine details.

Existing UAV aerial detection methods face critical trade-offs between accuracy and efficiency. While dual-stage detectors (e.g., MD-IPN\cite{li2022multi}, MS-FPN\cite{wang2021ms}) enhance multi-scale feature representation, their reliance on cascaded modules and image pyramids incurs prohibitive computational costs\cite{lin2017feature}. Lightweight single-stage approaches (e.g., APD\cite{zhang2024}, SPB-YOLO [7]) prioritize inference speed but suffer from degraded accuracy due to insufficient spatial detail preservation and inadequate handling of extreme scale variations. Moreover, deformable convolutions (e.g., Cascade ResNet50\cite{dai2017deformable}) improve viewpoint robustness but introduce parameter bloat. These limitations underscore the need for a unified framework that reconciles high precision, computational efficiency, and adaptability to deformable targets in resource-constrained UAV settings.

Our key Contributions:

1.	Quadruple Aggregation and Connection Feature Extractor (QACFE): A non-strided convolution module that hierarchically aggregates multi-scale features while preserving spatial granularity, addressing the information loss in existing downsampling heavy backbones.

2.	Decoupled Large-Kernel Convolutional Attention (DLKA): A lightweight attention mechanism that disentangles structural and edge-aware feature extraction via depthwise-separable large kernels, enhancing small target discriminability while mitigating computational overhead from expanded channel dimensions.

3.	Dual-Scale Fusion Framework: Integrates feature-level multi-scale extraction (via PAFPN) with pixel-level adaptive detection during inference, synergistically improving small target recall without sacrificing runtime efficiency.

4.	Deformable Detection Head with Learnable Offsets: Dynamically adjusts receptive fields to accommodate target deformation and scale shifts, achieving superior robustness compared to rigid anchor-based designs.

5.	Comprehensive Efficiency-Accuracy Balance: QLDNet achieves state-of-the-art accuracy (94.0\% AP) at 0.12s/image on aerial datasets, demonstrating 1.14× faster inference than baseline YOLOv8-n with fewer parameters, making it deployable on edge UAV platforms.

\begin{figure*} % picture
    \centering
    \includegraphics[width=0.75\textwidth]{fig11.png}
    \caption{Dual multi-Scale structure combined with QACFE,DLKA and DDH (QLDNet)}
    \label{figure:fig6}
\end{figure*}

\section{Method}
\label{Method}
\subsection{Dataset Selection and Preprocessing}
The HERIDAL dataset\cite{pyrro2022} was adopted for this study, comprising 1,647 high-resolution aerial images (4000 × 3000 pixels) captured under diverse real-world conditions. The dataset was collected at altitudes of 40–65 meters in Mediterranean wilderness regions of Croatia and Bosnia and Herzegovina. Images were acquired during daylight hours with varying natural lighting conditions (sunny, overcast, and partially shaded environments) to ensure robustness to illumination changes. The top-down vertical perspective and environmental diversity align with the operational scenarios outlined in Paul et al.\cite{paul2024smart}, ensuring practical relevance for UAV-based detection tasks.
The HERIDAL dataset is pre-partitioned into 1,546 training and 101 test images. While no dedicated validation set was provided, we implemented cross-validation during training by reserving 10\% of the training data for epoch-wise validation, as commonly practiced in lightweight detection frameworks\cite{kisantal2019augmentation}. This approach avoids overfitting while maximizing the utilization of limited annotated data. The test set size (101 images) was deemed statistically sufficient given the dataset’s high annotation density (3,229 labeled instances, averaging 3.2 targets per image), ensuring representative evaluation.
Original Pascal VOC XML annotations were converted to YOLO format to align with our training pipeline, preserving bounding box coordinates and class labels. Then,we Applied Contrast Limited Adaptive Histogram Equalization (CLAHE) with 64×64 pixel tiles to mitigate underexposure and overexposure artifacts while avoiding noise amplification. Our targeted Augmentation is as followed.
We use mosaic Augmentation in first N-10 Epochs: Combined four training images into a single composite to simulate crowded scenes and diversify backgrounds, enhancing multi-scale generalization. Then we introduced random rectangular occlusions (90\% probability) to improve robustness to partial target visibility, addressing real-world occlusion challenges as advocated by Paul et al.\cite{paul2024smart} We intentionally avoided generic augmentations (e.g. rotation, scaling) to preserve the geometric integrity of small targets (<50 pixels), which are highly sensitive to affine transformations in aerial contexts. [3]


\subsection{Proposed Solution}
To address the challenges of detecting small human targets characterized by varied poses under complex background interference, we propose a single-stage object detection architecture based on an integrated channel feature design with a deformable dual multi-scale detection head.

As shown in Figure \ref{figure:fig6},our QLDNet is a dual multiscale structure composed of quadruple aggregation and connected feature extract (QACFE), decoupled large kernel convolutional channel attention mechanism (DLKA), and deformable decoupled head (DDH).

\subsubsection{Dual Multi-Scale Detection of Features and Pixels}
An analysis of the label information discloses significant variation in the area of bounding boxes, indicating large differences in target sizes. In 4000 × 3000-pixel images, target widths and heights range from 70 to 150 pixels, resulting in considerable differences in corresponding feature map dimensions.

YOLOv8's Neck network combines the strengths of FPN(Feature Pyramid Network) and PAN(Path Aggregation Network), using bidirectional path aggregation to enrich feature maps with both contextual and semantic information. It incorporates multiple output layers, each corresponding to a specific feature scale. These multi-scale output layers allow the detection of targets of varying sizes, resolving the issue of size disparity from a feature multi-scale perspective.

Building upon feature multi-scale detection, this study proposes a pixel multi-scale detection method. As shown in Figure \ref{figure:fig5},the approach involves dividing the original image into input blocks of multiple resolutions and fusing the detection results across these pixel scales. This enhances detection accuracy by compensating for the detail loss often overlooked by feature multi-scale strategies.

\begin{figure}[htbp]
    \centering
    \includegraphics[width=\linewidth]{fig5.png}
    \caption{Overall Dual Multi-Scale Structure:Using a single-stage framework, our model's backbone creates multi-scale feature maps for various-sized targets. The neck fuses these maps to improve size adaptability to achieve multi-scale feature representation. Pixel-level Multi-scale fusion is employed at the pixel level, capturing more detailed information by adjusting the input image resolution through multi-resolution inputs.}
    \label{figure:fig5}
\end{figure}

Specifically, the method begins by segmenting the input image into smaller blocks, followed by independent object detection within each block. This segmentation strategy enables finer capture of target features in localized regions, addressing the limitations of conventional single-scale detection, which may overlook critical details. Subsequently, the detected targets in the image blocks are combined with the results from the original image. This integration is optimized using the Non-Maximum Suppression (NMS) algorithm.

The NMS algorithm retains the bounding box with the highest confidence score while suppressing other overlapping boxes with high intersection-over-union (IoU) values. This process minimizes redundant detections and improves the precision of the final detection results.

In this method, the key aspect of the NMS algorithm is quantifying the degree of overlap between bounding boxes, typically using IoU as the evaluation metric. By calculating the IoU values between the detection results from the smaller blocks and those from the original image, the method effectively selects the optimal bounding boxes while suppressing unnecessary overlapping detections. This ultimately achieves precise localization of the targets.

Additionally, this method enhances the model’s robustness to targets of different sizes through multi-scale feature extraction and multi-level feature fusion, improving both detection accuracy and speed.

\subsubsection{Quadruple aggregation and connected feature extract (QACFE) with decoupled large kernel convolutional channel attention mechanism (DLKA)}
We use the non-strided convolution combined with large-kernel convolution attention for extraction of deep multi-channel features.The formula for calculating the width \(O_H\) and height \(O_W\) of the feature map after convolution is given as shown in Equation(\ref{eq:convolution_output_height}) and Equation (\ref{eq:convolution_output_width}).

\begin{equation}
O_H = \frac{\text{Height} - \text{kernel\_size} + 2 \times \text{Padding}}{Stride} + 1
\label{eq:convolution_output_height}
\end{equation}
\begin{equation}
O_W = \frac{\text{Width} - \text{kernel\_size} + 2 \times \text{Padding}}{Stride} + 1
\label{eq:convolution_output_width}
\end{equation}
In the original framework, the backbone network uses strided convolutions with a stride of 2, which perform feature downsampling to extract deep features. With each convolution operation, the width and height of the feature map are reduced to half of their original size. However, for images where the bounding box area occupies only 0.02\%, such downsampling leads to unacceptable feature loss in tasks involving tiny object detection.

To address this issue, this study replaces strided convolutions with non-strided convolutions (stride = 1) and adopts a parallel combination of four pooling methods to achieve downsampling for deep feature extraction. Additionally, to prevent feature loss during the downsampling process, the results of the four pooling operations(as shown in Equation(\ref{max_pool})(\ref{avg_pool})(\ref{top-left})(\ref{bottom-Right})) are concatenated along the channel dimension of the feature map. The QACFE module first divides the input feature map into multiple 2×2 sub-blocks and performs pooling independently on each sub-block. The four pooling methods mentioned are Max Pooling, Average Pooling, Top-Left Pooling, and Bottom-Right Pooling. These four pooling operations are used for downsampling, and the results are concatenated along the channel dimension. For the first QACFE module of the backbone network, the input feature map has dimensions of 3*1538*1538, and the output feature map has dimensions of 12*769*769, with the number of channels increased to four times the original.
\begin{equation}
    y_{i,j}=\max_{p,q\in\mathrm{Window}}(x_{i+p,j+q})
    \label{max_pool}
\end{equation}
\begin{equation}
y_{i,j}=\frac{1}{k^2}\sum_{p,q\in\mathrm{Window}}x_{i+p,j+q}
\label{avg_pool}
\end{equation}
\begin{equation}
X_{\mathrm{top-left}}(i,j,c)=X(2i,2j,c)
\label{top-left}
\end{equation}
\begin{equation}
X_\text{bottom-Right}(i,j,c)=X(2i+1,2j+1,c)
\label{bottom-Right}
\end{equation}
We form a complementarity through the sparse high-response features of Max Pooling, the smooth contextual features of Average Pooling, and the local perturbation features of Top-Left Pooling and Bottom-Right Pooling, covering multi-dimensional attributes of the target and enhancing channel diversity of the features.This approach enables multi-dimensional extraction of channel features and ensures that fine-grained details are not excessively lost during downsampling.

\begin{figure*} % picture
    \centering
    \includegraphics[width=0.75\textwidth]{fig6.png}
    \caption{Quadruple Aggregation and Connected Feature Extractor (QACFE)}
    \label{figure:fig7}
\end{figure*}

The spatial resolution of an image is represented by its feature map in the width and height dimensions, describing the distribution and arrangement of pixels. Meanwhile, the feature map's channel dimension encodes depth information, capturing the responses and abstract features across different channels. During the downsampling phase in the backbone network, multi-channel features are thoroughly extracted. In subsequent network layers, the goal is to fully leverage these channel features.

Convolution is a fundamental technique in computer vision (CV) tasks, designed to extract local features by sliding convolutional kernels over an image and combining these features to construct deep feature representations. Convolution's advantages lie in its linear extraction of local features and its translation invariance, making it highly effective for feature extraction.

Large Kernel Convolution (LKC) extends traditional convolution by using larger convolutional kernels to capture features from a broader receptive field. A single LKC operation covers a larger region of the image, enabling the extraction of more extensive local features, such as structures and edges, which are typically low-frequency features. This capability enhances the model's understanding of channel features within the feature map. Moreover, the increased receptive field allows the network to gather more global information in a single operation, reducing the need for deeper networks and resulting in a shallower architecture. By decoupling a series of large, progressively expanding depth-wise convolutions, LKC constructs a network with a broader receptive field. This design generates features with various receptive field sizes, facilitating kernel selection for subsequent tasks. Additionally, the decoupled sequence design proves more effective than using a single large convolution kernel, significantly reducing the model's parameter count.

Attention mechanisms, originally developed for capturing semantic and contextual information in natural language processing (NLP) tasks, are incorporated into the model following feature extraction with LKC. After extracting features using LKC, a 1×1 convolution transforms the features into a weight matrix, enabling channel-wise attention computation to refine feature representation.
So we design the Decoupled Large Kernel Convolutional Attention (DLKA) in the feature fusion layer of the Neck section, such as the 13th layer after SPPF module. It enlarges the receptive field and captures long-range dependencies through detached large kernel convolutions. In brief, horizontal convolution (with a 1×11 kernel) is utilized to extract horizontal structural features, like the horizontal posture of a human body, whereas vertical convolution (with an 11×1 kernel) is used to capture vertical structural features, such as standing or lying postures. We set the stride to 11, padding to 55, and the activation function to SiLU. The outputs of horizontal and vertical convolutions are concatenated along the channel dimension, followed by a 1×1 convolution to adjust the number of channels, forming the DLKC module. After stacking two DLKC modules, the Squeeze-and-Excitation (SE) mechanism is applied to dynamically weight the important channels. We set the compression ratio to 16 (i.e., the intermediate layer dimension of the fully connected layer is C/16). The number of parameters is approximately 44.125C², where C represents the number of input channels. As shown in the ablation experiments in Table 3, the introduction of DLKA improves the mAP@0.5 of the model on the HERIDAL dataset by 0.9\% (from 68.87\% to 69.86\%), with only an 8.7\% increase in parameters (3.82M vs. 3.52M), demonstrating its efficiency. Moreover, it resolves the GPU memory out-of-memory issue caused by the addition of DDH and QACFE, proving its lightweight nature.
\begin{figure} % picture
    \centering
    \includegraphics[width=0.49\textwidth]{fig7.png}
    \caption{Decoupled Large Kernel Convolutional Channel Attention Mechanism. (a) Represents the horizontal receptive field constructed by 1×n horizontal convolution, with the red box indicating the actual target box, which extracts more precise local features for the lying people shown in the illustration; (b) Represents the vertical receptive field constructed by n×1 vertical convolution, with the red box indicating the actual target box, which also extracts more precise local features for the lying people shown in the illustration.}
    \label{figure:fig8}
\end{figure}

\subsubsection{Deformable Detection Head}

Although we have addressed the issue of excessive model parameters through improvements in network architecture, there remains a need to enhance the accuracy of object detection. This study achieves this by enriching the hierarchy of the detection head.To address deformation, a deformable detection head is employed. Standard convolutional kernels, typically structured as fixed 3×3 or 5×5 matrices, can only detect features in regular regions. The redesigned detection head introduces learnable offsets to each value in the convolution kernel matrix, enabling the network to capture information from irregular positions. The 2D convolution consists of two steps: 1) sampling using a regular grid \(R\) over the input feature map \(x\); 2)summation of sampled values weighted by \(w\).The grid \(R\) defines the receptive field size and dilation. \cite{dai2017deformable}For example,R is as shown in Equation(\ref{eq:3}):
\begin{equation}
    \mathcal{R} = \{(-1,-1), (-1,0), \ldots, (0,1), (1,1)\}
    \label{eq:3}
\end{equation}
defines a 3 × 3 kernel with dilation 1.
For each location \(\mathbf{p_0}\) on the output feature map \(y\),as shown in Equation(\ref{eq:placeholder_label}):
\begin{equation}
    y(\mathbf{p}_0) = \sum_{\mathbf{p}_n \in \mathcal{R}} \mathbf{w}(\mathbf{p}_n) \cdot \mathbf{x}(\mathbf{p}_0 + \mathbf{p}_n),
    \label{eq:placeholder_label}
\end{equation}
where \(\mathbf{p_n}\) enumerates the locations in \(R\).
In deformable convolution, the regular grid \(R\) is augmented with offsets {\(\Delta \mathbf{p_n}\mid n=1,…,N\)},where \(N=\mid R\mid\).Equation(3) becomes Equation(\ref{eq:y_p0}):
\begin{equation}
    y(\mathbf{p}_0) = \sum_{\mathbf{p}_n \in \mathcal{R}} \mathbf{w}(\mathbf{p}_n) \cdot \mathbf{x}(\mathbf{p}_0 + \mathbf{p}_n + \Delta \mathbf{p}_n).
    \label{eq:y_p0}
\end{equation}
Now, the sampling is on the irregular and offset locations \(\mathbf{p_n}+\Delta \mathbf{p_n}\).As the offset \(\Delta \mathbf{p_n}\) is typically fractional, Eq.(4) is implemented via bilinear interpolation as shown in Equation(\ref{eq:x_p}):
\begin{equation}
    x(\mathbf{p}) = \sum_{\mathbf{q}} G(\mathbf{q}, \mathbf{p}) \cdot x(\mathbf{q}),
    \label{eq:x_p}
\end{equation}
where \(p\) denotes an arbitrary (fractional) location (\(\mathbf{p}=\mathbf{p_0}+\mathbf{p_n}+\Delta \mathbf{p_n}\) for Equation(4)), q enumerates all integral spatial locations in the feature map \(x\), and \(G\)(·,·) is the bilinear interpolation kernel. Note that \(G\) is two dimensional. It is separated into two one dimensional kernels as shown in Equation(\ref{eq:G_qp}):
\begin{equation}
    G(\mathbf{q}, \mathbf{p}) = g(\mathbf{q_x}, \mathbf{p_x}) \cdot g(\mathbf{q_y}, \mathbf{p_y}),
    \label{eq:G_qp}
\end{equation}
where \( g(a, b) = \max(0, 1 - |a - b|) \). Eq. (5) is fast to compute as \( G(\mathbf{q}, \mathbf{p}) \) is non-zero only for a few \(\mathbf{q}\)s.
These offsets are learnable parameters, effectively allowing the convolution operation to provide the model with an adaptive receptive field. This makes the approach particularly suitable for handling geometric deformations in target objects.
\begin{figure} % picture
    \centering
    \includegraphics[width=0.5\textwidth]{fig10.png}
    \caption{Deformable decoupled head(DDH)}
    \label{figure:fig9}
\end{figure}

\section{Results}
\label{Results}
To evaluate the innovative contributions proposed in this study, we utilized a modified HERIDAL dataset with a resolution of 1568×1568 pixels to validate its effectiveness. A YOLO model was constructed using the open-source deep learning framework PyTorch and trained on a system equipped with an NVIDIA GeForce RTX 3090 GPU. During the training process, the initial learning rate was set to 0.01, and the Cosine Annealing Learning Rate scheduler (CosineAnnealingLR) was employed to adjust the learning rate, with a lower bound of 0.001. A weight decay parameter of 0.0005 was applied, and the Adam optimizer was selected. In the loss function, the bounding box loss was weighted at 70\%, while the classification loss accounted for 30\%. The model was trained for a total of 300 epochs, with a random seed set to 42 to ensure reproducibility. Each batch processed one sample, and four worker threads were enabled to accelerate data loading. Under these configurations, the model successfully achieved the training results.
\subsection{Dataset and Evaluation Metrics}
\begin{table*}[h]
    \centering
    \caption{Comparative Analysis of Training Evaluation Metrics on the HERIDAL Dataset}
    \label{tab:metrics_analysis}
    \begin{tabular}{l|cccccc}
        \hline
        \textbf{Method} & \textbf{Precision} (10\textsuperscript{-2}) & \textbf{Recall} (10\textsuperscript{-2}) & \textbf{mAP@0.5} (10\textsuperscript{-2}) & \textbf{mAP@0.5:0.95} (10\textsuperscript{-2}) & \textbf{FPS(Hz)}\\
        \hline
        Faster R-CNN\cite{ren2017} & 92.88 & 73.89 & 80.43 & 51.9 & 4.38\\
        YOLOX-l\cite{ge2021} & 88.22 & 73.89 & 77.34 & 48.39 & 23.7\\
        YOLOv5\cite{jocher2020} & 86.12 & 73.5 & 78.63 & 54.94 & 21.6\\
        RetinaNet\cite{lin2020} & 92.16 & 74.19 & 78.13 & 47.14 & 4.55\\
        SSD\cite{liu2016ssd} & 75.22 & 71.8 & 75.78 & 48.02 & 17.95\\
        CBAM\cite{woo2018} & \textbf{93.67} & 74.78 & 79.42 & 45.90  & 31.85\\
        ASFF\cite{liu2019} & 92.19 & 72.71 & 79.72 & 46.71 & 8.51\\
        RepVgg\cite{ding2021} & 93.66 & 72.69 & 79.16 & 44.3 & 15.87\\
        FPN\cite{lin2017} & 92.17 & 76.19 & 79.65 & 53.16 & 4.46\\
        MobileNetv3\cite{howard2019} & 92.87 & 72.11 & 74.92 & 47.33 & 16.1\\
        HorNet\cite{rao2022} & 87.94 & 74.75 & 79.25 & 48.40 & 7.59\\
        APDNet\cite{zhang2024} & 92.49 & 75.08 & 80.26 & 50.35 & 36.76 \\
        RFCD\cite{kundid2022} & 82.38 & - & - & - & 34.05\\
        Deformable DETR\cite{fang2021instances} & 92.52 & 74.19 & 77.62 & 48.75 & 3.24\\
        YOLOv8-s & 83.32 & 70.75 & 78.53 & 47.38 & 71.74 \\
        \textbf{ours} & \textbf{94.00} & \textbf{82.00} & \textbf{88.30} & \textbf{62.60}  & \textbf{82.10}\\
        \hline
    \end{tabular}
\end{table*}
\begin{table*}[h]
    \centering
    \caption{Comparative Analysis of Training Evaluation Metrics on the VTSaR Dataset}
    \begin{tabular}{lccccc}
        \toprule
        \textbf{Method} & \textbf{Precision (10$^{-2}$)} & \textbf{Recall (10$^{-2}$)} & \textbf{mAP@0.5 (10$^{-2}$)} & \textbf{mAP@0.5:0.95 (10$^{-2}$)} & \textbf{FPS (Hz)} \\
        \midrule
        Faster R-CNN & 95.49 & 94.2 & 95.84 & 45.57 & 2.6 \\
        YOLOX-l & 96.14 & 94.2 & 95.51 & 45.02 & 16.37 \\
        YOLOv5-x & 96.74 & 95.98 & 96.51 & 44.64 & 14.73 \\
        RetinaNet & 96.33 & 94.06 & 93.05 & 45.11 & 2.81 \\
        SSD & 94.38 & 93.48 & \textbf{98.31} & 39.73 & 11.21 \\
        CBAM & 95.60 & 94.69 & 94.69 & 42.98 & 18.12 \\
        ASFF & 96.56 & 93.17 & 92.47 & 43.22 & 9.6 \\
        RepVgg & 94.79 & 94.05 & 97.68 & 44.04 & 11.33 \\
        FPN & \textbf{98.48} & 92.74 & 95.80 & 46.52 & 2.72 \\
        MobileNetv3 & 93.37 & 91.92 & 97.34 & 38.47 & 12.41 \\
        HorNet & 96.53 & 89.10 & 96.66 & 45.88 & 4.42 \\
        APDNet & 94.97 & 95.04 & 95.57 & 46.19 & 39.06 \\
        \midrule
        RFCCD  & 93.88 & - & - & - & 27.18 \\
        Deformable DETR & 92.42 & 94.19 & 97.14 & 42.21 & 2.01 \\
        YOLOv8-s & 90.34 & 84.69 & 89.66 & 36.13 & 23.81 \\
        \textbf{ours} & \textbf{95.90} & \textbf{96.00} & \textbf{96.80} & \textbf{49.92} & \textbf{53.76} \\
        \bottomrule
    \end{tabular}
    \label{tab:vtsar_comparison}
\end{table*}
\subsubsection{Dataset and Preprocessing Methods}
The HERIDAL dataset consists of aerial images of Mediterranean landscapes\cite{bozic2019}. It was constructed based on expert knowledge and statistical data compiled by Koester\cite{koester2008} and the Croatian SAR team to ensure realistic coverage of search-and-rescue (SAR) scenarios. The dataset includes 1,647 annotated high-resolution drone-captured images of the Mediterranean wilderness in Croatia and Bosnia, comprising 1,546 training images and 101 test images. An example of the dataset is shown in Figure \ref{figure:fig4}.

Similar to SAR-APD training data, this dataset contains only one foreground class, “person.” All individuals are annotated with bounding boxes using the VOC XML label format\cite{everingham2010}. During the data preprocessing phase, all XML files in the VOC dataset were iterated through, and normalized center coordinates and bounding box dimensions were generated for each target label. These annotations were then reformatted into the YOLO-compatible .txt format,as show in Equation(\ref{eq:8})(\ref{eq:9})(\ref{eq:10})(\ref{eq:11}):

\begin{equation}
c_x = \frac{x_{\min} + x_{\max}}{2 \times width}
\label{eq:8}
\end{equation}
\begin{equation}
c_y = \frac{y_{\min} + y_{\max}}{2 \times height}
\label{eq:9}
\end{equation}
\begin{equation}
w = \frac{x_{\max} - x_{\min}}{2 \times width}
\label{eq:10}
\end{equation}
\begin{equation}
h = \frac{y_{\max} - y_{\min}}{2 \times height}
\label{eq:11}
\end{equation}
Each image in the HERIDAL dataset contains annotations for at least one individual, with some images featuring more than ten annotations. In total, HERIDAL includes 3,229 person annotations. The high-resolution aerial images were captured using various drone and camera models across multiple locations, with flight altitudes ranging from 40 to 65 meters. Most images have a resolution of 4000×3000 pixels, while a few have a widescreen resolution of 4000×2250 pixels. Additionally, the majority of the images were taken from a top-down vertical perspective, as described in Section 2.2.1.

In other words, the ground resolution of the aerial images is approximately 2 cm per pixel (i.e., each pixel corresponds to roughly 2 cm on the ground)[12]. Considering the relatively high flight altitude, this resolution is remarkably accurate. The average object size is around 60×60 pixels, accounting for only 0.03\% of the total image area. Clearly, such extremely small average object sizes pose significant challenges for most object detection methods, which is a central focus of this study.
\subsubsection{Evaluation Metrics}
To evaluate the proposed recognition algorithm from various perspectives, this study primarily employs the following metrics:
\begin{enumerate}
    \item Precision:This metric measures the proportion of predicted positive cases (i.e., detected objects) that are actually positive. It evaluates the accuracy of the model’s predictions. The formula for calculating precision is as shown in Equation(\ref{eq:precision}):
\begin{equation}
    \label{eq:precision}
    Precision = \frac{TP}{TP + FP}
\end{equation}
Where:

 TP(True Positives): The number of samples correctly predicted by the model as positive.

 FP(False Positives): The number of samples incorrectly predicted by the model as positive.
    \item Recall:Recall refers to the proportion of actual positive samples that are correctly predicted as positive by the model. It evaluates the model’s ability to identify all positive samples. The formula for calculating recall is as shown in Equation(\ref{eq:recall}):
\begin{equation}
    \label{eq:recall}
    Recall = \frac{TP}{TP + FN}
\end{equation}
Where:

 FN(False Negatives): The number of positive samples that the model incorrectly predicts as negative.

    \item Mean Average Precision (mAP):mAP is the mean of the Average Precision (AP) values across all classes. AP represents the average precision at different recall levels and is typically calculated when the IoU is greater than or equal to 50\%. For each class, the Precision-Recall curve is plotted based on the prediction results, and the average precision across all recall levels is computed to obtain the AP. The mean of the AP values for all classes is then calculated to derive the mAP.

\end{enumerate}

\subsection{Comparative Experiments on Methods}
To evaluate the performance of the proposed algorithm, this study conducted 300 epochs of human target detection experiments on the HERIDAL dataset, comparing the results in terms of precision, recall, and mean Average Precision (mAP). The epoch number is based on the convergence of loss curves as show in Figure \ref{figure:fig-new1}.
\begin{figure} % picture
    \centering
    \includegraphics[width=0.48\textwidth]{fig12.jpg}
    \caption{Training & Validation Loss(left) and mAP50 Evaluation Metric(right)}
    \label{figure:fig-new1}
\end{figure}
The default resolution of the detector could lead to memory overflow; thus, the original images were resized to 1536 × 1536 before training.We use 2× NVIDIA GeForce RTX 3090 GPUs (24GB VRAM), with shared computational resources with Ubuntu 22.04.5 LTS (Linux 6.5.0-28-generic kernel).The framework is an isolated python virtual environment via Anaconda, with PyTorch 2.1.0.We set batch\_size=1, num\_workers=4,  pin\_memory=True,  random\_seed=42, learning\_rate=0.001. (To ensure consistency across experimental conditions, neither the proposed algorithm nor the comparative algorithms used pre-trained models on large datasets during training.)

The proposed method was compared against single-stage detection algorithms (YOLOX-1, YOLOv5, SSD, RetinaNet), two-stage detection algorithms (Faster R-CNN), large-model-based algorithms (Deformable DE-TR), and algorithms incorporating attention mechanisms and network structure optimizations (CBAM, ASFF, RepVGG, HorNet). The results of the comparative object detection experiments are shown in Table \ref{tab:metrics_analysis}. Data that could not be retrieved are denoted by “-,” and the bold values indicate the best performance for each metric.

Compared to the two-stage Faster R-CNN algorithm, the proposed method achieved a 1.12\% improvement in precision, an 8.11\% increase in recall, and a 7.87\% gain in mAP under IoU is greater than or equal to 50\% conditions. When compared to the single-stage RetinaNet algorithm, the proposed method improved precision by 0.33\%, recall by 7.81\%, and mAP under IoU is greater than or equal to 50\% by 10.17\%.

We have supplemented the test results on the VTSaR dataset. It can be seen in Table \ref{tab:vtsar_comparison} that our method achieves the highest metrics in terms of recall,  mAP@0.5: 0.95,  and FPS (Frames Per Second). The precision and mAP@0.5 metrics are within 1-3\% of the highest values. The reason for this may be the insufficient clarity of individual images in the dataset. Moreover, the SSD algorithm, which has the best mAP @0.5 metric, and the FPN algorithm, which has the best precision metric, both fall far short of our method in terms of FPS. Therefore, our method not only ensures the best real-time performance but also has a small gap from the best accuracy, which better meets the needs of practical applications.Based on a series of experimental results from the newly added dataset, we believe that the model has strong generalization capability.

\begin{table*}[h]
\centering
\caption{the lightweight versions of YOLOv9 serious methods, Nano Det and ours comparsion on HERIDAL dataset}
\begin{tabular}{lcccccc}
\toprule
Methods & {Precision (\%)} & {Recall (\%)} & {mAP@0.5 (\%)} & {mAP@0.5:0.95 (\%)} & {Model size(KB)} & {FPS(HZ} \\
\midrule
YOLOv9-T & 87.00 & 73.07 & 79.04 & 52.61 & 6760 &  23.26\\
YOLOv9-S & 91.90 & 81.60 & 86.20 & 54.6 & 14691 & 19.92 \\
YOLOv9-M & 91.10 & 84.30 & 86.40 & 53.3 & 39722 & 21.93 \\ 
YOLOv9-C & 93.50 & 82.80 & 85.10 & 52.6 & 50271 & 19.31 \\
NanoDet & {---} & 55.1 & 78.4 & 41.9 & 7542 & 15.15 \\
ours & 94.00 & 82.00 & 88.30 & 62.6 & 8422 & 82.10 \\ 
\bottomrule
\label{the lightweight versions}
\end{tabular}
\end{table*}
We supplement YOLOv9-T, YOLOv9-S, YOLOv9-M, YOLOv9-C models and Nano Det to train and test the dataset for comparative experiments, with the results as shown in Table \ref{the lightweight versions}.The precision of QLDNet at 94.0\% is significantly better than other YOLOv9 variants, such as YOLOv9-M's 91.10\%. In terms of recall, QLDNet (82.0\%) is close to YOLOv9-M (84.3\%) but significantly higher than Nano Det (55.1\%), indicating that our model's miss rate for small objects is at an acceptable level. QLDNet's mAP@0.5:0.95 reaches 62.6\%, substantially outperforming other models, demonstrating its robustness under strict IoU thresholds.

The model size of QLDNet is 8422 KB, which is only 57.3\% of YOLOv9-S and 16.7\% of YOLOv9-C, making our more lightweight model more suitable for deployment on edge devices.

Although YOLOv9-T and NanoDet are lightweight models, they significantly lag behind QLDNet (62.6\%) in terms of recall, mAP@0.5, and mAP@0.5:0.95 metrics, and their inference speeds are slower. We believe this is because, despite having fewer parameters, YOLOv9-T and NanoDet rely on architectures with computationally intensive designs such as standard convolutions and dense connections, resulting in higher FLOPs required for processing a single image and thus slower inference speeds. In contrast, our QLDNet model employs the DLKA layer, which separates horizontal and vertical convolutions, reducing the computational complexity of standard large-kernel convolutions from $O(k^2 \cdot C_{\text{in}} \cdot C_{\text{out}})$ to $O(k \cdot C_{\text{in}} \cdot C_{\text{out}})$, where $k$ is the size of the kernel, significantly decreasing the computational load.

\subsection{Ablation Study and Quantitative Analysis}
To evaluate the contribution of individual network components to performance, this study conducted human target detection experiments on the HERIDAL dataset. After 100 training epochs, the results were compared in terms of precision, recall, and mean Average Precision (mAP@0.5). (To ensure consistency in experimental conditions, neither the proposed algorithm nor the comparative algorithms utilized pre-trained models on large datasets during training.)
% Requires: \usepackage{booktabs}
As shown in Table \ref{tab:module_ablation_effects}, incorporating the QACFE module into the backbone network, which transfers spatial dimension features to the channel dimension to enrich deep-level features, led to an improvement in precision by 5.79\%, recall by 3.46\%, and mAP@0.5 by 2.52\%. Additionally, modifying the decoupled detection head (DDH) with deformable convolution increased precision by 1.55\% and mAP@0.5 by 1.09\%.

These results confirm the performance enhancements achieved by stacking the QACFE module and DDH in the network’s neck and head sections. However, combining these components resulted in a large parameter count of 4,129,251 bits. Moreover, the multi-channel feature maps extracted through four different methods were not fully utilized. To reduce the parameter count and improve feature map utilization, a decoupled large-kernel convolutional attention mechanism was introduced, dividing the convolution into horizontal and vertical operations. By comparing the metrics of models using only the QACFE module, only the large-kernel convolutional attention mechanism, and the combination of both, it was concluded that their combined effect exceeded the sum of their individual contributions. This approach not only reduced the parameter count but also improved multiple performance indicators.

After 300 training epochs, the proposed method achieved a precision of 94\%, a recall of 82.00\%, and an mAP@0.5 of 88.30\%. Furthermore, as the model was trained without using the 22.87 MB pre-trained weight file from the COCO dataset, the training started from scratch. As a result, the final weight file size was significantly smaller, at only 8.39 MB, making it more suitable for edge devices with limited storage capacity.

Additionally, this study introduced a dual multi-scale approach combining pixel-level and feature-level multi-scale methods, with pixel-level multi-scaling applied during the inference phase. The input image was divided into 80 sub-images of 600 × 600 pixels with a 0.25 overlap ratio. These sub-images were independently processed, and their detection results were merged with the original image detections using Non-Maximum Suppression (NMS) to produce the final output. This approach allowed the detection of smaller objects that might be undetectable in the original image due to their relatively smaller true area but became detectable after scaling.

\begin{figure*} % picture
    \centering
    \includegraphics[width=0.95\textwidth]{fig8.png}
    \caption{(a) Original detection effect, where the target exists but cannot be detected in the blue box; (b) Detection effect after cropping, with the difference from the original detection effect being that the target in the blue box can now be detected.}
    \label{figure:fig10}
\end{figure*}
\begin{table*}[h]
    \centering
    \caption{Comparison Table of Module Ablation Effects.}
    \label{tab:module_ablation_effects}
    \begin{tabular}{l|cccc}
        \toprule
        \textbf{Method} & \textbf{P(\%)} & \textbf{R(\%)} & \textbf{mAP\newline @0.5(\%)} & \textbf{Parameter} \\
        \midrule
        YOLOv8-n               & 72.91 & 63.24 & 67.78 & 3157200 \\
        +QACFE               & 78.70 & 66.70 & 70.30 & 3602640 \\
        +DDH                 & 74.46 & 63.23 & 68.87 & 3104133 \\
        DDH+DLKA             & 79.88 & 63.55 & 69.86 & 3549573 \\
        DDH+QACFE            & -     & -     & -     & 4129251 \\
        DDH+DLKA\newline +QACFE       & \textbf{82.63} & \textbf{69.26} & \textbf{78.71} & \textbf{3822469} \\
        \bottomrule
    \end{tabular}
\end{table*}

\subsection{Attention Module Parameter Comparison Experiment}
To expand the receptive field of convolutional feature extraction and aggregate both local feature information and global relational information, a decoupled large-kernel convolution was employed in this algorithm. To evaluate the effect of kernel size on detection performance, experiments were conducted with kernel sizes of K = 7, 11, 23, 35, and 41 over 100 training epochs while keeping other variables constant.

As shown in Table \ref{tab:kernel_size}, the variations in mAP@0.5 for different kernel size values were recorded. Based on the results in Table \ref{tab:kernel_size}, the best detection performance was achieved when K=11. Therefore, the kernel size parameter kernel size in this study was set to 11.
\begin{table}[h]
    \centering
    \caption{Comparison of Different Values of K on the HERIDAL Dataset}
     \label{tab:kernel_size}
    \begin{tabular}{c|cc}
        \toprule
        \textbf{Kernel\_size} & \textbf{Precision(\%)} & \textbf{TF} \\
        \midrule
        7  & 92.7 & 209 \\
        11 & \textbf{96.2} & 211 \\
        23 & 92.4 & 207 \\
        35 & 90.9 & 200 \\
        41 & 93.7 & \textbf{214} \\
        53 & 90.7 & 208 \\
        \bottomrule
    \end{tabular}
    \label{table:k_comparison}
\end{table}
\subsection{Comparison Experiment on the Number of Deep Feature Extraction Modules}
To reduce the number of parameters in the model and facilitate deployment on edge devices, the proposed algorithm utilizes stacked QACFE modules during the downsampling process for extracting deep high-level features. Additionally, newly introduced learnable offsets in the detection head increase the parameter count. To evaluate the impact of the number of added modules (M) on detection performance, experiments were conducted with M=2, 4, 6, 7, and 8 on the dataset.

As shown in Table \ref{tab:module_num}, the variation in the size of the weight file generated by the algorithm for different values of M is presented. Based on the results in Table \ref{tab:module_num}, the best overall detection performance was achieved when M=8. Therefore, the parameter MMM was set to 8 in this study, corresponding to 6 QACFE modules stacked with DLKA and the detection head modified to DDH.

\begin{table*}[h]
    \centering
    \caption{Comparison of Different Values of M on the HERIDAL Dataset}
    \label{tab:module_num}
    \begin{tabular}{l|cccc}
        \toprule
        \textbf{Module}&\textbf{M}&\textbf{Weight File Size(KB)}&\textbf{Inference Time for 100 Images(s)}&\textbf{Parameter} \\
        \midrule
        YOLOv8-n & 0 & 7060 & 13.94 & 3,104,133 \\ 
        QACFE & 2 & 9821 & 3.96 & 4,736,355 \\ 
        QACFE & 4 & 7613 & 5.04 & 3,333,603 \\ 
        QACFE & 6 & 7853 & 3.66 & 3,456,483 \\ 
        QACFE+DDH & 7 & - & - & 4,129,251 \\ 
        QACFE+DDH+DLKA & 8 & 8586 & 12.18 & 3,822,469 \\ 
        \bottomrule
    \end{tabular}
    \label{tab:comparison_m_values}
\end{table*}

As shown in Table \ref{tab:module_num}, modifying the detection head based solely on QACFE stacking resulted in exceeding the GPU memory capacity. However, after incorporating the large-kernel convolutional attention module into the improved detection head, the model not only enhanced its accuracy in recognizing human targets with varied poses but also achieved a 1.76-second improvement in processing speed per 100 images compared to the baseline model. Additionally, the model file size increased by only 1.526 MB, remaining within the storage constraints of edge devices.

\subsection{Qualitative Analysis of Experimental Results}
Figure \ref{figure:fig11} compares the results of the proposed QLDNet method with those of YOLOv8 after training for 300 epochs under identical experimental conditions. The figure showcases a representative set of images, including horizontal and vertical lying postures as well as crouching or sitting postures captured from various angles in a top-down perspective. The results demonstrate that the proposed algorithm outperforms in terms of detection accuracy and robustness to deformed targets.We demonstrate the detection capabilities of the baseline and our method in three scenarios: Complex background, Brightly lit, and Dimly lit. The images in the first column represent the Ground truth, with the red boxes drawn with thin lines indicating the true label positions. The images in the second column show the detection results of the Baseline, while the third column presents the detection results of our method, both indicating the category and detection confidence. The first three rows of images show that in complex backgrounds with interference from stones, grass, and surface textures, our method provides more comprehensive detection results with higher confidence. The fourth row of images illustrates that in brightly lit conditions, the baseline fails to detect a high-exposure light-colored clothed person, whereas our algorithm exhibits stronger robustness and can detect the target person sitting on the right wearing white. The fifth row of images demonstrates that in dimly lit scenarios, our method's detection confidence fluctuates less, performing stably under complex conditions, making it suitable for applications where high robustness is required.

\begin{figure} % picture
    \centering
    \includegraphics[width=0.48\textwidth]{fig9.png}
    \caption{Comparison of recognition results between other algorithm and QLDNet algorithm.}
    \label{figure:fig11}
\end{figure}

\begin{figure} % picture
    \centering
    \includegraphics[width=0.48\textwidth]{fig13.png}
    \caption{A statistical analysis of lightweight evaluation metrics for detection models, where ground truths (GT), true positives (TP), false positives (FP), and false negatives (FN) are visually represented by color blocks: green for GT, red for TP, yellow for FP, and blue for FN.}
    \label{figure:fig13}
\end{figure}

\begin{figure} % picture
    \centering
    \includegraphics[width=0.48\textwidth]{fig14.png}
    \caption{Visualization results of comparative experiments with lightweight models, where ground truths (GT), true positives (TP), false positives (FP), and false negatives (FN) are visually represented by color blocks: green for GT, red for TP, yellow for FP, and blue for FN.}
    \label{figure:fig14}
\end{figure}

As shown in the Figure \ref{figure:fig13} above, in the first column of images, the detection result of Faster R-CNN includes one False Positive sample, the detection result of Deformable DETR has multiple False Negative samples, and the detection result of RetinaNet has multiple False Negative samples and two False Positive samples. APDNet and our method perform better. In the second column of images, the detection result of Deformable DETR includes both False Negative and False Positive samples. In the third column of images, the detection results of Faster R-CNN, Deformable DETR, and APDNet all have false detections, i.e., False Positive samples, with only our method showing good detection performance.

As shown in the aforementioned Figure \ref{figure:fig14}, YOLOv9-S and YOLOv9-T exhibit significant false positives (FP) and false negatives (FN) due to their small model size and feature loss during downsampling, which impairs their ability to distinguish between background and objects. YOLOv9-C and YOLOv9-M demonstrate satisfactory detection performance on the first image but struggle with standing-posture targets in the second image. This discrepancy may stem from their detection heads being insensitive to pose variations, coupled with the low proportion of standing targets in the image. In contrast, our proposed model achieves robust detection performance across both images, effectively handling targets in diverse postures.

\section{Discussion}
The experimental results presented in this study demonstrate the effectiveness of the proposed QLDNet architecture in addressing the challenges of small human target detection in aerial imagery. The integration of dual multi-scale analysis, non-strided convolution, and large-kernel convolution attention mechanisms has significantly improved the model's ability to detect small and deformed targets, which are often missed by traditional detection algorithms. Below, we discuss the key findings and their implications.
\subsection{Performance Improvements}
The proposed QLDNet model achieved a precision of 94\%, a recall of 82.00\%, and an mAP@0.5 of 88.30\%, outperforming several state-of-the-art algorithms, including Faster R-CNN, YOLOv5, and RetinaNet. These improvements are particularly notable given the small size of the targets in the HERIDAL dataset, where the average object size is only 60x60 pixels, occupying just 0.03\% of the image area. The dual multi-scale approach, which combines pixel-level and feature-level multi-scale detection, proved to be highly effective in capturing fine-grained details of small targets, which are often lost in traditional single-scale detection methods.

The non-strided convolution combined with large-kernel convolution attention also played a crucial role in reducing feature loss during downsampling. By preserving fine-grained information and enhancing the model's ability to capture global contextual information, these modifications significantly improved the model's robustness to target deformation and occlusion. The deformable detection head further enhanced the model's adaptability to irregular target shapes, making it particularly effective in handling the complex poses often encountered in aerial imagery.

\subsection{Computational Efficiency and Edge Deployment}
One of the key objectives of this study was to develop a lightweight model suitable for edge deployment. The proposed QLDNet achieved this by reducing the parameter count and optimizing the model's architecture. The final weight file size of 8.39 MB is significantly smaller than many existing models, making it feasible for deployment on edge devices with limited storage capacity. Additionally, the model's inference time of 12.18 seconds for 100 images is competitive, especially considering the high detection accuracy achieved.

The use of stacked QACFE modules and the decoupled large-kernel convolutional attention mechanism not only improved detection performance but also reduced the model's computational complexity. This balance between accuracy and efficiency is crucial for real-time applications, such as search-and-rescue operations, where both speed and precision are critical.

\subsection{Limitations and Future Work}
While the proposed QLDNet model has demonstrated significant improvements in small target detection, there are still some limitations that need to be addressed. First, the model's performance on extremely low resolution images or images with severe occlusion remains a challenge. Future work could explore more advanced data augmentation techniques or generative adversarial networks (GANs) to improve the model's robustness in such scenarios.

Second, the current model is trained and evaluated on the HERIDAL dataset, which primarily consists of aerial images from Mediterranean landscapes. To ensure broader applicability, future research should consider testing the model on datasets from different geographical regions and environmental conditions. This would help validate the model's generalizability and adaptability to diverse real-world scenarios.

Finally, while the model's lightweight design is suitable for edge deployment, further optimization could be explored to reduce the inference time even further. Techniques such as model quantization or pruning could be investigated to achieve faster processing speeds without compromising detection accuracy.

\section{Conclusion}
\label{Conclusion}
This study proposed QLDNet, a lightweight and efficient network for detecting extremely small human targets in aerial imagery, by integrating non-strided convolution,decoupled large-kernel convolutional attention (DLKA), and deformable detection heads (DDH). The dual multi-scale strategy, combining pixel-level and feature-level fusion, significantly improved the detection accuracy for targets occupying as little as 0.03\% of the image area. Experimental results on the HERIDAL dataset demonstrated state-of-the-art performance, achieving an mAP@0.5 of 88.30\% with a compact model size of 8.39 MB, suitable for edge deployment. The proposed QACFE module effectively preserved spatial details during downsampling, while DLKA enhanced structural feature extraction through horizontal and vertical large-kernel convolutions.

Despite its advancements, QLDNet exhibits the following limitations in extreme scenarios. Extremely Low-Resolution Inputs: When targets are captured at resolutions below 50×50 pixels, the model struggles to extract discriminative features due to insufficient pixel-level details. Severe Occlusion: Partial or complete occlusion of targets (e.g., under rubble or vegetation) leads to missed detections, as the model relies heavily on visible structural cues. Adverse Lighting Conditions: Performance degrades under strong shadows, overexposure, or nighttime environments, where target-background contrast is reduced.

To address these limitations, future work will focus on multi-modal fusion and adaptive network design. Integrating thermal infrared or LiDAR data to complement RGB inputs in low-visibility scenarios. Self-supervised pre-training on unlabeled aerial imagery to enhance feature generalization.
QLDNet’s lightweight design and real-time capability make it a practical solution for drone-based search-and-rescue missions. Future iterations could extend its application to wildlife monitoring, precision agriculture, and infrastructure inspection, further advancing the role of AI in remote sensing.


The data that support the findings of this article are not publicly available due to privacy. They can be requested from the author at 185721@stu.hebut.edu.cn.

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