Explainable Deep Anomaly Detection with Sequential Hypothesis Testing for Robotic Sewer Inspection
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866912511035441152 |
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| author | George, Alex Shepherd, Will Tait, Simon Mihaylova, Lyudmila Anderson, Sean R. |
| author_facet | George, Alex Shepherd, Will Tait, Simon Mihaylova, Lyudmila Anderson, Sean R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22546 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable Deep Anomaly Detection with Sequential Hypothesis Testing for Robotic Sewer Inspection George, Alex Shepherd, Will Tait, Simon Mihaylova, Lyudmila Anderson, Sean R. Robotics 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. |
| title | Explainable Deep Anomaly Detection with Sequential Hypothesis Testing for Robotic Sewer Inspection |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.22546 |