Full automation of sewer CCTV surveys - Renewal

South West Water (SWW) and the whole UK water industry fight a constant battle to maintain and rehabilitate their ageing wastewater networks. To effectively do so, water companies regularly inspect their sewer pipes with CCTV, recording footage of a pipe’s interior. This enables a trained technician to assess a pipe’s condition, annotating faults and features within the recorded footage. Thanks to the monotonous and subjective nature of this task, the process is time consuming and prone to human error. Combating this, the award-winning work of this fellowship utilises machine learning and Artificial Intelligence (AI) techniques to improve the accuracy and efficiency of this existing surveying process.
This project has developed software for the automatic annotation of faults and their associated meta-data including shape, location, and defect code in line with industry standards. Furthermore, in an effort to identify a “gold standard” dataset for benchmarking AI techniques, we have begun to objectively uncover the surprisingly large inconsistencies in surveying practices. This unanticipated outcome has already prompted further investigation into the reliability of human annotation, and the further scope for AI to yield accurate and consistent annotations.
In parallel, the project has already secured routes to commercial exploitation via project partner iTouch Systems. SWW’s implementation of the tool conservatively predicts an annual efficiency saving of £250k per year. On the other hand, the licensing and sale of the product predicts a year one gross profit of £150k, growing to £700k by year five.
Renewal of this fellowship, will ensure the impact of this technology, further accelerating its current trajectory and enabling its seamless integration with current industry practices. This is reflected by the proposal’s renewed objectives:
1) Optimise the developed algorithms for use on low power portable and robotic devices.
2) Incorporate further historic...