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Remote Sensing
Volume 14
Issue 20
10.3390/rs14205268
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Article

YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module

by
Simin Wang
1,2,
Song Gao
1,2,*,
Lun Zhou
1,2,
Ruochen Liu
1,2,
Hengsheng Zhang
1,2,
Jiaming Liu
1,2,
Yong Jia
1,2 and
Jiang Qian
3
1
Key Laboratory of Earth Exploration and Infomation Techniques (Chengdu University of Technology), Ministry of Education, Chengdu 610059, China
2
The College of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China
3
The School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(20), 5268; https://doi.org/10.3390/rs14205268
Submission received: 21 September 2022 / Revised: 17 October 2022 / Accepted: 18 October 2022 / Published: 21 October 2022
(This article belongs to the Special Issue Microwave Remote Sensing for Object Detection)
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Abstract

As an outstanding method for ocean monitoring, synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain little feature information. It is difficult to differentiate them from the background clutter, and there is the problem of a low detection rate and high false alarms. To improve the detection accuracy for small ships, we propose an efficient ship detection model based on YOLOX, named YOLO-Ship Detection (YOLO-SD). First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 datasets show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in terms of overall performance.
Keywords: synthetic aperture radar (SAR); small ship detection; deep learning; YOLOX synthetic aperture radar (SAR); small ship detection; deep learning; YOLOX
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MDPI and ACS Style

Wang, S.; Gao, S.; Zhou, L.; Liu, R.; Zhang, H.; Liu, J.; Jia, Y.; Qian, J. YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module. Remote Sens. 2022, 14, 5268. https://doi.org/10.3390/rs14205268

AMA Style

Wang S, Gao S, Zhou L, Liu R, Zhang H, Liu J, Jia Y, Qian J. YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module. Remote Sensing. 2022; 14(20):5268. https://doi.org/10.3390/rs14205268

Chicago/Turabian Style

Wang, Simin, Song Gao, Lun Zhou, Ruochen Liu, Hengsheng Zhang, Jiaming Liu, Yong Jia, and Jiang Qian. 2022. "YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module" Remote Sensing 14, no. 20: 5268. https://doi.org/10.3390/rs14205268

APA Style

Wang, S., Gao, S., Zhou, L., Liu, R., Zhang, H., Liu, J., Jia, Y., & Qian, J. (2022). YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module. Remote Sensing, 14(20), 5268. https://doi.org/10.3390/rs14205268

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