ZHOU Qian-qian,SITU Zu-xiang,TENG Shuai,et al.Intelligent Detection and Classification of Drainage Pipe Defects Based on Convolutional Neural Networks[J].China Water & Wastewater,2021,37(21 21):114-118.
Intelligent Detection and Classification of Drainage Pipe Defects Based on Convolutional Neural Networks
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第37卷
Number:
21 21
Page:
114-118
Column:
Date of publication:
2021-11-01
- Keywords:
- drainage pipe defect; convolutional neural networks; artificial intelligence; detection and classification
- Abstract:
- Traditional drainage pipe defect detection needs a lot of manpower. To cope with the problem, a system for automatic detection and evaluation of the drainage pipeline defects was established based on an artificial intelligence algorithm—convolutional neural networks (CNN). Six common pipeline defects (crack, disjoint, obstacle, residual wall, tree root and normal category) observed by CCTV video images were learned, trained and tested by the model. The training and validation accuracies of the CNN model were 100% and 97%, respectively, and the average recognition accuracy of the six kinds of pipeline defects reached 90%, which proved that the established model could well identify the defect types without the need of relevant detection expertise. The CNN model had a high confidence in the detection of the tree roots and disjoints, followed by the residual walls and cracks, and the classification accuracy of the obstacles and the normal type was the lowest. The deep learning is feasible in the field of automatic detection of the drainage pipe defects, and the model has good generalization ability, which provides a scientific and accurate detection tool for the detection of the pipe defects.
Last Update:
2021-11-01