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| Research on Deep Learning-Based Rotation Detection Algorithms for Ship Wakes in SAR Images |
| XIA Yilin1，2， LIU Gang3， YAN Congqiang4， CAI Yunze1，2，5 |
| 1. School of Automation and Intelligent Sensing， Shanghai Jiao Tong University， Shanghai 200240， China；
2. Key Laboratory of System Control and Information Processing， Ministry of Education of China，
Shanghai Jiao Tong University， Shanghai 200240， China； 3. Shanghai Institute of Satellite
Engineering， Shanghai 201109， China； 4. China Institute of Aeronautical Radio Electronics，
Shanghai 200241， China； 5. State Key Laboratory of Submarine Geoscience，
Shanghai Jiao Tong University， Shanghai 200240， China |
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Abstract This paper proposed a deep learning-based rotated bounding box detection algorithm for ship wake detection in synthetic aperture radar (SAR) images. The proposed algorithm addressed the issue of background pixel redundancy in horizontal bounding box detection algorithms and the complex design of traditional detection methods, which fail to identify curved wakes effectively. The overall network framework of the algorithm consisted of three core components: a feature extraction module, a feature fusion module, and a prediction head network. The feature extraction module was responsible for extracting key feature information from the input SAR images. The feature fusion module further integrated these features to enhance the model's perception of the wake morphology. Finally, the prediction head network would provide precise target localization based on the fused features. The results of the rotated bounding box detection were acquired, including the center point position and rotation angle. Experimental results show that compared to other rotated target detection algorithms, the proposed algorithm achieves higher accuracy in SAR image ship wake detection tasks and effectively distinguishes between targets and backgrounds, thus accomplishing the task of SAR image ship wake detection under various scenarios.
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Received: 04 March 2025
Published: 31 October 2025
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2025, Vol. 8 
