Ship detection in SAR image based on improved YOLOv5 network

Cheng-ge Fang; Ying Bi; Zhen-yu Wu; Hui Wang; Zi-wei Chen

doi:10.1117/12.2674533

23 May 2023 Ship detection in SAR image based on improved YOLOv5 network

Cheng-ge Fang, Ying Bi, Zhen-yu Wu, Hui Wang, Zi-wei Chen

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126041X (2023) https://doi.org/10.1117/12.2674533
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

In order to improve the accuracy of YOLO series algorithm in detecting small ship targets in SAR images, a target detection algorithm based on improved yolov5 is proposed in this paper. In this paper, The Multi-Scale Channel Attention Module (MS_CAM) is added to the network structure to aggregate local and global feature information in the way of channel attention, which can alleviate the problem of large semantic gap between different scales to a certain extent. In addition, the PANet fusion structure in YOLOv5 was replaced by BiFPN structure to make the network better weight of learning features. The experiment on the open RSDD-SAR dataset shows that compared with the traditional method, the AP value and recall rate of the whole dataset are improved.

Citation Download Citation

Cheng-ge Fang, Ying Bi, Zhen-yu Wu, Hui Wang, and Zi-wei Chen "Ship detection in SAR image based on improved YOLOv5 network", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126041X (23 May 2023); https://doi.org/10.1117/12.2674533

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