Automated quantification of sewage pipe cracks using deep learning for urban water environment management

Sewage pipe defects can significantly affect the urban water environment, like leakage of pollutants through pipe cracks to groundwater. Currently, sewage pipe defects are detected mainly through closed-circuit television inspection, which is conducted manually and is time-consuming. This study proposes an integrated deep-learning-based algorithm to detect and quantify pipe cracks from images, namely the Crack Detection and Characterization (CDC) method. The method is based on models created in two steps (i) crack detection by semantic segmentation, and (ii) crack length quantification using an innovative algorithm. The CDC algorithm is verified by images both artificially created in the laboratory and from actual inspection. For both laboratory and field cases, the CDC method is verified precisely. From the results, the CDC method exhibited a higher accuracy in crack identification and length quantification than other existing models. The results also show that the deblurring process can greatly improve accuracy. This study can contribute to decision-making in sewage pipe maintenance and water environment management by providing an innovative way of more efficient and accurate pipe defect assessment compared with traditional labor-intensive work.