Work

Explainable underwater target recognition models: principles, methods, and applications

Year: 2025

Type: article

Abstract: 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.

Source: Frontiers in Physics

DOI: 10.3389/fphy.2025.1682253

Authors: Tianyang Xu, Hongjian Jia, Jixing Qin

Institutions: Heilongjiang University, Chinese Academy of Sciences, Institute of Acoustics, State Key Laboratory of Acoustics

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,077

Funders: Chinese Academy of Sciences, State Key Laboratory of Acoustics