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<p>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. QLDNet is a dual multiscale network based on quadruple aggregation and connected feature extract (QACFE), decoupled large kernel convolutional channel attention mechanism (DLKA), and deformable decoupled head (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&#xa0;s per image, meeting the system’s real-time requirements with fewer parameters and satisfying processing speeds. The code can be find at <a href="https://github.com/Sjl185721/QLDNet-v1.git">https://github.com/Sjl185721/QLDNet-v1.git</a>.</p>

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QLDNet: a lightweight and efficient network for high-robustness aerial human detection in UAV-based remote sensing

  • 期刊论文

  • 发表日期：

    2025年8月29日

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  • Mandun Zhang,
  • Jiale Shi,
  • Chenyue Hou,
  • Yonghui Pang,
  • Yiqi Zhang,
  • Xiangsheng Huang

摘要

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. QLDNet is a dual multiscale network based on quadruple aggregation and connected feature extract (QACFE), decoupled large kernel convolutional channel attention mechanism (DLKA), and deformable decoupled head (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 s 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.