@inproceedings{59de034b0edf407da1db9b19d9826ca5,
title = "SAR Ship OOD Detection Combining Deep Learning with Guidance and Constraint Mechanisms",
abstract = "Synthetic Aperture Radar (SAR) [1] is an active ground detection system capable of performing all-weather, all-time surveillance of the Earth's surface [2]. However, the Out-Of-Distribution (OOD) [3] problem causes a significant degradation in the performance of traditional deep learning algorithms. This paper designs a SAR ship detection method combining deep learning with Guidance and Constraint mechanisms. Based on the original deep learning model, CFAR preprocessing results are introduced as guidance to assist the model in more accurately localizing and extracting more precise features. Additionally, the salient features of SAR are employed as a constraint mechanism to filter out irrelevant features. Experimental results show that the proposed method outperforms a pure deep learning model, especially when there are significant distribution differences between the training and validation sets. It demonstrates higher robustness and stronger generalization ability for out-ofdistribution (OOD) samples.",
keywords = "CFAR, OOD, deep learning, ship target detection",
author = "Qiansheng Ma and Zhe Chen and Yun Zhang and Zhiquan Ding and Qing Hua",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE Radar Conference, RadarConf 2025 ; Conference date: 04-10-2025 Through 09-10-2025",
year = "2025",
doi = "10.1109/RadarConf2559087.2025.11205048",
language = "英语",
series = "Proceedings of the IEEE Radar Conference",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "367--372",
editor = "Marek Rupniewski and Shannon Blunt and Jacek Misiurewicz and Greco, \{Maria Sabrina\} and Braham Himed",
booktitle = "Proceedings of the 2025 IEEE Radar Conference, RadarConf 2025",
address = "美国",
}