空天防御2025，Vol.8Issue(5)：64-74,11.
基于深度学习的SAR图像舰船尾迹旋转框检测算法研究
Research on Deep Learning-Based Rotation Detection Algorithms for Ship Wakes in SAR Images
摘要
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.关键词
SAR图像/深度学习/舰船尾迹检测Key words
SAR Images/deep learning/ship wake detection分类
电子信息工程引用本文复制引用
夏伊琳,刘刚,鄢丛强,蔡云泽..基于深度学习的SAR图像舰船尾迹旋转框检测算法研究[J].空天防御,2025,8(5):64-74,11.基金项目
航空科学基金项目(20220001057001,20240001057002) （20220001057001,20240001057002）
