用可解释深度学习+序列检验，自动识别管道故障并定位异常。
Explainable Deep Anomaly Detection with Sequential Hypothesis Testing for Robotic Sewer Inspection
- 结合可解释模型与序列概率检验，实现空间定位与时间证据融合。
- 在真实管道数据上，检测准确率显著优于传统方法。
- 适合智能巡检、工业质检等需要可信诊断的场景。
管道故障（如泄漏和堵塞）可能导致地下水污染、财产损失和服务中断。传统检查依赖人工审阅移动机器人采集的CCTV视频，效率低且易出错。为实现自动化，我们提出一种新系统：结合可解释深度学习异常检测与序列概率比检验（SPRT）。异常检测器处理单帧图像，提供可解释的异常空间定位；SPRT则对图像序列进行时间证据聚合，提升对噪声的鲁棒性。实验表明，该系统在真实管道数据上显著提升了异常检测性能，验证了时空联合分析在可靠、稳健的管道巡检中的有效性。
原文摘要 · Abstract (English)
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.
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