MINI REVIEW article
Front. Phys.
Sec. Physical Acoustics and Ultrasonics
Explainable underwater target recognition models: principles, methods, and applications
Provisionally accepted
- 1Electrical Engineering College, Heilongjiang University, Harbin, China
- 2State Key Laboratory of Acoustics and Marine Information, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China, Beijing, China
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With the increasing strategic importance of the ocean, underwater intelligent systems have become essential for signal processing, target recognition, and autonomous navigation. The widespread application of deep learning has significantly advanced underwater acoustic missions, but its "black box" nature has led to critical concerns about decision explainability, limiting its trustworthy application in high-risk scenarios. This paper provides a systematic review of explainable models for underwater target recognition, elaborating on the core concepts and main methods of explainability. It also reviews research progress and representative achievements in sonar imaging, signal analysis, and autonomous navigation. Finally, future directions, including causal reasoning, cross-modal collaboration, and physical knowledge integration, are identified to provide a reference for developing safe and reliable underwater intelligent systems.
Keywords: underwater intelligent perception, Underwater target recognition, artificial intelligence, Explainable artificial intelligence, explainability in deep learning
Received: 08 Aug 2025; Accepted: 05 Nov 2025.
Copyright: © 2025 Xu, Jia and Qin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hongjian Jia, jiahongjian@hlju.edu.cn
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