Deep learning-assisted automated sewage pipe defect detection for urban water environment management

Abstract

A healthy sewage pipe system plays a significant role in urban water management by collecting and transporting wastewater and stormwater, which can be assessed by hydraulic model. However, sewage pipe defects have been observed frequently in recent years during regular pipe maintenance according to the captured interior videos of underground pipes by closed-circuit television (CCTV) robots. In this case, hydraulic model constructed based on a healthy pipe would produce large deviations with that in real hydraulic performance and even be out of work, which can result in unanticipated damages such as blockage collapse or stormwater overflows. Quick defect evaluation and defect quantification are the precondition to achieve risk assessment and model calibration of urban water management, but currently pipe defects assessment still largely relies on technicians to check the CCTV videos/images. An automated sewage pipe defect detection system is necessary to timely determine pipe issues and then rehabilitate or renew sewage pipes, while the rapid development of deep learning especially in recent five years provides a fantastic opportunity to construct automated pipe defect detection system by image recognition. Given the initial success of deep learning application in CCTV interpretation, the review (i) integrated the methodological framework of automated sewage pipe defect detection, including data acquisition, image pre-processing, feature extraction, model construction and evaluation metrics, (ii) discussed the state-of-the-art performance of deep learning in pipe defects classification, location, and severity rating evaluation (e.g., up to ~96 % of accuracy and 140 FPS of processing speed), and (iii) proposed risk assessment and model calibration in urban water management by considering pipe defects. This review introduces a novel practical application-oriented methodology including defect data acquisition by CCTV, model construction by deep learning, and model application, provides references for further improving accuracy and generalization ability of urban water management models in practical application.

Keywords: Automated pipe defect detection; Deep learning; Hydraulic model calibration; Image recognition; Urban water management.