[1]ZHAO Wenqing,KANG Yijin,ZHAO Zhenbing,et al.A remote sensing image object detection algorithm with improved YOLOv5s[J].CAAI Transactions on Intelligent Systems,2023,18(1):86-95.[doi:10.11992/tis.202203013]
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A remote sensing image object detection algorithm with improved YOLOv5s

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 1 Page number: 86-95 Column: 学术论文—智能系统 Public date: 2023-01-05
Title:
A remote sensing image object detection algorithm with improved YOLOv5s
Author(s):
ZHAO Wenqing1; 2;  KANG Yijin1;  ZHAO Zhenbing3;  ZHAI Yongjie1
1. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China;
2. Engineering Research Center of the Ministry of Education for Intelligent Computing of Complex Energy System, Baoding 071003, China;
3. School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
remote sensing images; objects of interest; object detection; feature extraction; efficient channel attention structure; multiscale feature fusion; contextual information; Swin Transformer; coordinate attention mechanism
CLC:
TP751;TP391
DOI:
10.11992/tis.202203013
Abstract:
Aiming at the low average target detection accuracy in remote sensing images caused by obscure features in the objects of interest, complex background information, and multiple small targets, we propose a new remote sensing image object detection algorithm with improved YOLOv5s (Swin-YOLOv5s). First, an efficient channel attention structure is added to the convolutional block of the backbone feature extraction network to suppress the interference of irrelevant information; second, cross-scale connection and contextual information weighting operations are performed to enhance detection target feature extraction on the basis of multiscale feature fusion, and the fused feature maps are composed into a new feature pyramid; finally, the Swin Transformer structure and coordinate attention mechanism are used to further enhance the semantic information and global perception ability of small targets. The result of a feature fusion elimination experiment performed on the DOTA and RSOD datasets shows that the proposed algorithm can significantly improve the average accuracy of object detection in remote sensing images.
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