面向无人机图像小目标检测的轻量化异构协同主干双路桥接网络

doi:10.16451/j.cnki.issn1003-6059.202511007

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Abstract

To address the issues of highly dense small targets, severe occlusion in UAV images, and high computational complexity of existing general detection models, a lightweight heterogeneous collaborative dual-path bridged network(LHCB-Net) for small target detection in UAV images is proposed in this paper. Based on the YOLOv9 framework, the functionally homogeneous stacking and the limited receptive field of RepNCSPELAN4 are decoupled. A backbone network is constructed by combining the reparameterized convolution block RepVCBlock and the global attention large selective kernel network(Galsk). Thus, a three-dimensional attention mechanism covering channel, space and global contextual dependencies is integrated to effectively enhance the perception of small targets. Galsk combines global modeling and feedforward enhancement mechanisms to improve feature extraction in complex backgrounds and occlusion scenarios and compensate for the receptive field reduction caused by lightweight design. Moreover, secondary backbone features are directly connected to the detection head through cross-layer bridging to achieve multi-scale feature fusion and optimize localization accuracy. Additionally, a scale-adaptive IoU loss function is introduced to dynamically adjust regression weights for targets of different scales. Experimental results on VisDrone2019, UAVDT, and a self-built dataset demonstrate that LHCB-Net significantly improves detection performance for dense small targets while reducing parameters and computational cost, providing an efficient solution for real-time onboard detection. The complete code is available at: https://github.com/tson122556/LHCB-Net/tree/master.

Key words： Unmanned Aerial Vehicle Image Small Target Detection Lightweight Network Machine Learning Computer Vision

Received: 20 September 2025

ZTFLH:

TP391.413-39

Fund:

Supported by Natural Science Foundation of Shaanxi Province(No.2023-JCYB-562)

Corresponding Authors: CHEN Junying, Ph.D.,associate professor. Her research interests include machine learning, intelligent detection, and visual perception information processing.

About author:: XIE Kaijing, Master student. His research interests include target detection from the perspective of unmanned aerial vehicles and intelligent robots(unmanned aerial vehicles).

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Cite this article:

XIE Kaijing,CHEN Junying. Lightweight Heterogeneous Collaborative Dual-Path Bridged Network for Small-Target Detection in UAV Images[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(11): 1041-1054.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202511007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I11/1041

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