The detection of Pipeline defects is the prerequisite for the overhaul and maintenance of urban drainage pipelines.Accurate defect detection technology is conducive to promoting the quality of urban pipeline construction projects.Compared with traditional defect detection methods,deep learning algorithms can improve the accuracy and reliability of defect detection.Based on the measured defect data of pipelines,this study employs the image semantic segmentation algorithm for the detection of drainage pipeline defects and the multi-classification algorithm for the detection of drainage pipeline defects based on anomaly detection and semi-supervised learning.The detection performance of the two algorithms for pipeline defects is compared and analyzed taking the detection accuracy,precision and recall rate of three types of pipeline defects,misalignment,rupture and concealed connections of branch pipes,as the evaluation indicators.It is found that the image semantic segmentation algorithm for drainage pipeline defect detection is an ideal pipeline defect detection model.
NETL
NSTL
万方数据
