An automatic defect detection and localization method using imaging geometric features for sewer pipes

Accurate longitudinal localization of defects in sewer pipes is crucial for efficient maintenance and renewal. However, traditional closed-circuit television (CCTV)-based localization methods have been inefficient and imprecise, impeding real-time inspection performance. In this paper, we firstly point out that the inaccuracy of CCTV-based localization methods stems from the oversight of monocular imaging geometry, which leads to a deviation in sewer defect localization. Then, we propose an automated framework for sewer defect detection and longitudinal localization based on a deep-learning algorithm and monocular imaging geometry. Specifically, structural defects are qualitatively analyzed to explore their intrinsic correlation with the pipe wall. Then, the imaging geometry is leveraged to establish the projection relationship between the defects and the pipe joints. The longitudinal localization correction model for structural defects is then developed using the pipe diameter as the actual size. Our experiments show that this framework enhances the mean average precision (mAP) for multidefects by 7.2 % and achieves a theoretical mean absolute error of 0.2 m and a practical mean absolute error of 0.28 m for defect localization, surpassing current research. Finally, with the generated detection results and localization records, the proposed framework can promote the efficiency of the sewer investigation and accelerate the development of intelligent sewer systems.