Unsupervised Domain-Adaptive SAR Ship Detection Based on Cross-Domain Feature Interaction and Data Contribution Balance

被引:3
|
作者
Yang, Yanrui [1 ,2 ]
Chen, Jie [1 ,2 ]
Sun, Long [1 ]
Zhou, Zheng [3 ]
Huang, Zhixiang [2 ]
Wu, Bocai [1 ]
机构
[1] China Elect Technol Grp Corp, Res Inst 38, Hefei 230088, Peoples R China
[2] Anhui Univ, Sch Elect & Informat Engn, Key Lab Intelligent Comp & Signal Proc, Minist Educ, Hefei 230601, Peoples R China
[3] Natl Univ Def Technol NUDT, Coll Elect Sci & Technol, Key Lab Complex Electromagnet Environm Effects Ele, Changsha 410073, Peoples R China
基金
中国国家自然科学基金;
关键词
deep learning; SAR; object detection; unsupervised domain adaptation; OBJECT DETECTION; ADAPTATION; ALGORITHM; ALIGNMENT; IMAGES;
D O I
10.3390/rs16020420
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Due to the complex imaging mechanism of SAR images and the lack of multi-angle and multi-parameter real scene SAR target data, the generalization performance of existing deep-learning-based synthetic aperture radar (SAR) image target detection methods are extremely limited. In this paper, we propose an unsupervised domain-adaptive SAR ship detection method based on cross-domain feature interaction and data contribution balance. First, we designed a new cross-domain image generation module called CycleGAN-SCA to narrow the gap between the source domain and the target domain. Second, to alleviate the influence of complex backgrounds on ship detection, a new backbone using a self-attention mechanism to tap the potential of feature representation was designed. Furthermore, aiming at the problems of low resolution, few features and easy information loss of small ships, a new lightweight feature fusion and feature enhancement neck was designed. Finally, to balance the influence of different quality samples on the model, a simple and efficient E12IoU Loss was constructed. Experimental results based on a self-built large-scale optical-SAR cross-domain target detection dataset show that compared with existing cross-domain methods, our method achieved optimal performance, with the mAP reaching 68.54%. Furthermore, our method achieved a 6.27% improvement compared to the baseline, even with only 5% of the target domain labeled data.
引用
收藏
页数:21
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