High-speed acquisition and intelligent tunnel surface defects recognition

Abstract

We introduce a framework to efficiently capture and identify the defects on the subway tunnel surfaces. Specifically, we design a high-speed multi-camera-synchronous acquisition system for scanning the tunnel surfaces and capturing the cracks from images. We propose an Improved Cascade Region-Based Convolutional Neural Network (R-CNN) method to locate and classify defects, with the crack being refined by image processing algorithms. The methodology is tested by on-site experiments carried out in an operational subway. The mAP value reaches 0.827, with a specific AP value of 0.625 and a newly proposed measure identification rate of 0.942 for crack objects. Notably, by training on bounding box labels, our overall algorithm exhibits the lowest Error Rate (ER) and the highest Intersection over Union (IoU) across all test images, surpassing the performance of classical semantic segmentation networks trained on pixel-level labels.