基于实例分割+CCTV排水管道缺陷检测方法研究

当前排水管道检测普遍采用CCTV管道闭路电视检测系统，但系统在缺陷判断过程存在人工参与度高，检测效率低、主观误差大等问题。本文提出一种基于实例分割算法结合CCTV排水管道缺陷检测方法，采用CCTV检测系统采集管道图像，基于Mask R-CNN卷积神经网络排水管道缺陷实例分割检测，对破裂、变形、腐蚀、沉积、障碍物、树根缺陷进行检测分类，并对其中破裂缺陷进行检测评级。实验结果表明，本文方法现场实验缺陷检测准确率达到了100%，现场实验破裂缺陷检测高阶分类准确度达到100%，表现出较好检测性能。

Research of Drainage Pipeline Defect Detection Method Based on Instance Segmentation and CCTV

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.