The performance of Automated Recognition (ATR) algorithms on side-scan sonar imagery has shown to degrade rapidly when deployed on non benign environments. Complex seafloors and acoustic artefacts constitute distractors in the form of strong textural patterns, creating false detections or preventing detections of true objects. This paper presents two online seafloor characterisation techniques to improve explainability during Autonomous Underwater Vehicles (AUVs) missions. Importantly and as opposed to previous work in the domain, these techniques are not based on a model and require limited input from human operators, making it suitable for real-time onboard processing. Both techniques rely on an unsupervised machine learning approach to extract terrain features which relate to the human understanding of terrain complexity. The first technnique provides a quantitative, application-driven terrain characterisation metric based on the performance of an ATR algorithm. The second method provides a way to incorporate subject matter expertise and enables contextualisation and explainability in support for scenario-dependent subjective terrain characterisation. The terrain complexity matches the expectation of seasoned users making this tool desirable and trustworthy in comparison to traditional unsupervised approaches. We finally detail an application of these techniques to repair a Mine Countermeasures (MCM) mission carried with SeeByte autonomy framework Neptune.

翻译：自动识别算法在侧扫声纳图像上的性能在非友好环境中部署时已显示出快速退化。复杂海底和声学伪影以强纹理模式的形式构成干扰，导致虚警或阻碍真实目标的检测。本文提出两种在线海底表征技术，以提升自主水下航行器任务期间的可解释性。重要的是，与领域内先前工作不同，这些技术不基于模型且需要操作员有限的输入，使其适用于实时机载处理。两种技术均依赖无监督机器学习方法提取与人类对地形复杂性理解相关的地形特征。第一种技术基于自动识别算法性能提供一种定量、应用驱动的地形表征度量。第二种方法提供融入领域专业知识的方式，并支持场景依赖的主观地形表征的上下文化和可解释性。相较于传统无监督方法，该地形复杂度符合资深用户的预期，使该工具更受青睐且可信。最后，我们详细阐述了这些技术在修复由SeeByte自主框架Neptune执行的水雷对抗任务中的应用。