説明可能な深層異常検知と逐次仮説検定によるロボット下水道検査
Explainable Deep Anomaly Detection with Sequential Hypothesis Testing for Robotic Sewer Inspection
下水道検査ロボットの映像に対し、説明可能な深層学習による異常検知と逐次確率比検定を組み合わせ、時空間的に頑健な異常検出を実現した。
著者: Alex George, Will Shepherd, Simon Tait, Lyudmila Mihaylova, Sean R. Anderson
分類: cs.RO
原文アブストラクト
Sewer pipe faults, such as leaks and blockages, can lead to severe consequences including groundwater contamination, property damage, and service disruption. Traditional inspection methods rely heavily on the manual review of CCTV footage collected by mobile robots, which is inefficient and susceptible to human error. To automate this process, we propose a novel system incorporating explainable deep learning anomaly detection combined with sequential probability ratio testing (SPRT). The anomaly detector processes single image frames, providing interpretable spatial localisation of anomalies, whilst the SPRT introduces temporal evidence aggregation, enhancing robustness against noise over sequences of image frames. Experimental results demonstrate improved anomaly detection performance, highlighting the benefits of the combined spatiotemporal analysis system for reliable and robust sewer inspection.