关于特征归属的漂移检测用于监测海港监控中的视觉强化学习模型
Francisco Javier Iriarte1,2, Beatrice Azoubel1, Adrián Carrizo-Pérez1
1Fundación Vicomtech, Basque Research and Technology Alliance (BRTA), Donostia/San Sebastián, Gipuzkoa, 20009, Spain.
Open research Europe
|February 2, 2026
概括
通过使用可解释AI (XAI) 来监控深度强化学习 (DRL) 模型,FADMON提高了海上安全. 这种方法可以检测概念漂移,改善对不断变化的威胁的监控系统.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 海上活动的增长需要先进的港口安全,以检测非法贩运.
- 复杂的走私需要先进的监控技术,比如SMAUG项目的技术.
- 监控中的深度强化学习 (DRL) 模型是有效的,但缺乏透明度,阻碍了持续模型监测 (CMM).
研究的目的:
- 引入FADMON,一种可解释AI (XAI) 驱动的方法,用于在基于图像的模型中检测概念漂移.
- 通过实现半监督,可解释的模型监控来增强CMM.
- 在模拟的海上港口监控环境中验证FADMON的有效性.
主要方法:
- FADMON使用特征归属的统计漂移测试来识别政策偏差.
- 一个三个阶段的模型监控架构支持半监督可解释的监控.
- 该方法在模拟的海上场景中使用SMAUG的DRL模型进行了验证.
主要成果:
- 在所有测试的场景中,FADMON准确地检测到漂移 (平均p值为0.000).
- 与其他方法相比,非漂移场景显示出更高的平均p值 (0.553±0.215).
- 性能指标与已建立的漂移检测方法相匹配.
结论:
- FADMON为监控监控系统提供了一个可解释层.
- 它检测模型解释的变化,监测概念,而不仅仅是数据.
- 在海上安全方面,FADMON为CMM提供了一个强大的,可解释的解决方案.
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