Deep learning based automatic defect identification method for underground pipeline

Abstract

The invention discloses a deep learning based automatic defect identification method for an underground pipeline. Positive and negative sample sets, of the underground pipeline, needed by training ofconvolutional neural network (CNN) are prepared; the sample sets are preprocessed, and modified, in a batch manner, to the uniform size of 300*300, data improvement is carried out, and sample data fortraining is generated; a structure of the CNN is designed, training is carried out, and a weight connection matrix W during network convergence is obtained and used for a detection process later; aimed at video data, first and last 10 frames of a video are eliminated, a defect target frame is selected roughly, and key frames are sampled from the video every 10ms; each sampling frame of the videois input into the CNN, and whether there is a defect is determined; and according to a result of each frame in the last step, whether the video includes defects is concluded. According to the method,the utilization rate of data is improved, characteristics of a defect pipeline image are learned automatically via the convolutional network, and automatic identification for the defect pipeline is realized.

Description

technical field [0001] The invention relates to an automatic defect identification method, in particular to an automatic identification method for underground pipeline defects based on deep learning. Background technique [0002] The underground pipeline defect recognition system involves many disciplines and computer technology issues such as pattern recognition and computer vision. It is a relatively complex and huge project. It has a broad application prospect in many aspects such as the detection, maintenance and reconstruction of municipal underground facilities. Although there are preliminary research results now, there are still many difficulties, such as low efficiency and low recognition rate in traditional methods. At the same time, the environment of underground pipelines is complex, and the differences between pipeline defects are small, which makes the automatic identification of underground pipeline defects a very complicated problem and faces many challenges. ...

AI Technical Summary

Problems solved by technology

This method has the disadvantages of slow detection speed and insufficient accuracy.

Images