[1]SHEN Zhenyu,ZHU Fenghua,WANG Zhixue,et al.Uav aerial image target detection based on high-efficiency feature extraction and large receptive field[J].CAAI Transactions on Intelligent Systems,2025,20(4):813-821.[doi:10.11992/tis.202405001]
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Uav aerial image target detection based on high-efficiency feature extraction and large receptive field

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 4 Page number: 813-821 Column: 学术论文—机器学习 Public date: 2025-08-05
Title:
Uav aerial image target detection based on high-efficiency feature extraction and large receptive field
Author(s):
SHEN Zhenyu1;  ZHU Fenghua2;  WANG Zhixue1;  SHEN Zhen2;  XIONG Gang2
1. School of Rail Transit, Shandong Jiaotong University, Ji’nan 250300, China;
2. National Key Laboratory of MultiModal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Keywords:
drone aerial images; small target detection; feature extraction; multi-scale variation; YOLOv8; context information; receptive field; loss function
CLC:
TP391.4
DOI:
10.11992/tis.202405001
Abstract:
Aiming at the problems of small targets, target occlusion and complex background in UAV aerial images, a target detection network based on high-efficiency feature extraction and large receptive field (EFLF-Net) was proposed. Firstly, the missed detection rate of small targets was reduced by optimizing the detection layer architecture. Then, the new building blocks were integrated in the backbone network to improve the efficiency of feature extraction. Then, a content-aware feature recombination module and a large selective kernel network were introduced to enhance the context-aware ability of the neck network for occluded targets. Finally, the Wise-IoU loss function was used to optimize the bounding box regression stability. Experimental results on the VisDrone2019 dataset show that EFLF-Net improves the average precision by 5.2% compared with the basic algorithm. Compared with the existing representative target detection algorithms, the proposed method has better detection effects for UAV aerial images with small targets, mutual occlusion of targets and complex backgrounds.
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