Research on Pipeline Defect Detection Based on Optimized Faster R-cnn Algorithm

Abstract

Aiming at the problem of low efficiency and high labor intensity in manual inspection of drainage pipes, a method of defect inspection of drainage pipes based on optimized Faster-Rcnn algorithm is proposed. Faster R-CNN is an algorithm proposed for target detection this year, which is based on the deep learning network model of region-based recommendation network (RPN). This paper analyzes the implementation of RPN network in Faster R-CNN algorithm, optimizes the network and introduces K-Means clustering method. By clustering all Anchors in the training set and inputting the clustering results into the RPN network, the training of the network can be accelerated and the recognition accuracy of the algorithm can be improved. The experimental results show that the accuracy of the algorithm is up to 92.4%, which has great application value. This research has important reference significance to promote the automatic detection of sewage pipeline defects.