Yuqian Mei, Zhiying Zhang, Liang Sun, and Yunpeng Li
OpenCV, hollow nano drainage pipes, pipeline defects, detection robot, CNN, Canny
The urban sewage pipeline rupture and leakage can seriously affect the daily life of residents. The defect detection of drainage pipelines is an important support for the continuous functioning and later maintenance of pipelines. The internal environment of the wastewater drainage pipeline is poor. The existing defect detection methods have limited ability to extract image features. The accuracy of the detection methods cannot meet the expected requirements. Based on this, a pipeline defect detection robot is designed based on defect image analysis of waste drainage pipelines. For defect image recognition in robots, the Canny algorithm is improved to pre-process defect images. Then, the improved Mask-region-convolutional neural network (Mask-RCNN) model is constructed to extract the processed defect image features. According to the findings, the defect image recognition model constructed in the study converges only after 12 iterations. The accuracy of image recognition can be maintained at around 98.85%. The robot defect detection rate based on this image recognition model is 100%. This indicates that the proposed detection robot based on improved defect image feature recognition can better complete defect detection of drainage pipelines, providing support for drainage pipeline maintenance.
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