一个可解释的适应环境的融合和专家在循环评估框架,用于水下声纳图像分类
Kamal Basha S1, Anukul Kiran B1, Athira Nambiar1
1Department of Computational Intelligence, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203, India.
The Journal of the Acoustical Society of America
|February 18, 2026
概括
我们开发了一个可解释的AI系统,用于声纳图像分类,改善水下物体识别. 该系统使用了新的融合框架和专家评估,以提高对声纳应用AI的信任和可解释性.
科学领域:
- 人工智能的人工智能
- 海洋技术 海洋技术
- 计算机视觉 计算机视觉
背景情况:
- 声纳图像解释对于水下物体检测至关重要.
- 传统的深度学习模型与声纳特定特征 (例如声学阴影) 斗争,缺乏透明度.
- 这种不透明性限制了性能和用户对人工智能驱动的声纳分析的信任.
研究的目的:
- 提出一个可解释的声纳图像分类系统,解决当前深度学习模型的局限性.
- 为了提高AI在声纳应用中的可解释性和可靠性.
- 合并专门的分类器,以改善声纳特征提取和分析.
主要方法:
- 开发了一种新的上下文适应融合框架 (CAFF),通过基于注意力的融合整合了Naive,ShadowNet和HighlightNet分类器.
- 嵌入式可解释性技术:梯度加权类激活映射 (Grad-CAM),夏普利添加式解释 (SHAP) 和局部可解释模型不可知解释 (LIME).
- 实施了专家在循环评估,使用AQUA-X框架进行验证和改进.
主要成果:
- CAFF有效地融合了声纳特有的特征,优于传统方法.
- 可解释性技术为声纳特定特征的解释提供了详细的见解.
- 通过AQUA-X的专家验证证实了系统的可解释性,并确定了用于声纳分析的最佳AI技术.
结论:
- 拟议的CAFF和AQUA-X框架显著提高了用于声纳图像解释的AI的可靠性和透明度.
- 这种方法促进了可靠的AI解决方案,用于现实世界的水下探索和物体识别.
- 该研究在海洋声学等专业领域推进了可解释的AI (XAI) 应用.
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