Deep learning based method of longitudinal dislocation detection for metro shield tunnel segment

This paper presents a longitudinal dislocation detection method using an accurate tunnel segment joint labeling algorithm featured by deep CNNs (Convolutional Neural Networks). This method is proposed to be four steps. First, a mobile scanning system is used to acquire 3D point clouds of metro shield tunnels. Then, we use cylinder projection to generate tunnel surface depth images from 3D point clouds for segment joint labeling. Subsequently, two deep CNNs are designed to accurately label the segment joints on the depth images. The first CNN can roughly locate the segment joint positions, and the second precisely label the segment joints. Based on the labeled segment joints, two point data sets are obtained on both sides of each segment joint. By using the RANSAC algorithm, the two point sets can fit into two planes, the equation of which is then calculated to generate the dislocation value of the tunnel segment. Experiment results show that this method can label segment joints integrally and accurately without being affected by nearby tunnel equipment. Compared with traditional image edge detection algorithms (Canny and Sobel with Hough Transform), the CNNs are more powerful in labeling segment joints. When the distance measuring accuracy of scanner is 1.2 mm + 10 ppm, the internal and external accuracy of our detection method are evaluated to be 0.4 mm and 0.9 mm respectively. Compared with the scanning line method, the external accuracy of our method is higher and more reliable when there is tunnel equipment around segment joints.

Automated summary

This paper presents a longitudinal dislocation detection method using deep CNNs, which accurately labels segment joints without being affected by nearby tunnel equipment. Experimental results show that the method has higher external accuracy compared to the scanning line method when there is tunnel equipment around segment joints.