Currently, CCTV pipeline closed-circuit television inspection systems are generally used to detect drainage pipes, but the system has problems such as high manual participation in the defect judgment process, low detection efficiency, and large subjective errors. This paper proposes a method based on instance segmentation algorithm combined with CCTV drainage pipeline defect detection method, using CCTV inspection system to collect pipeline images, based on Mask R-CNN convolutional neural network drainage pipeline defect instance segmentation detection, cracking, deformation, corrosion, deposition, obstacles The defects of objects and tree roots shall be inspected and classified, and the cracking defects shall be inspected and rated. The experimental results show that the on-site test defect detection accuracy rate reaches 100%, and high-level classification accuracy of cracked defect detection in field experiment reaches 100%, showing good detection performance.