Automatic defect detection of metro tunnel surfaces using a vision-based inspection system

Abstract

Due to the impact of the surrounding environment changes, train-induced vibration, and human interference, damage to metro tunnel surfaces frequently occurs. Therefore, accidents caused by the tunnel surface damage may happen at any time, since the lack of adequate and efficient maintenance. To our knowledge, effective maintenance heavily depends on the all-round and accurate defect inspection, which is a challenging task, due to the harsh environment (e.g., insufficient illumination, the limited time window for inspection, etc.). To address these problems, we design an automatic Metro Tunnel Surface Inspection System (MTSIS) for the efficient and accurate defect detection, which covers the design of hardware and software parts. For the hardware component, we devise a data collection system to capture tunnel surface images with high resolution at high speed. For the software part, we present a tunnel surface image pre-processing approach and a defect detection method to recognize defects with high accuracy. The image pre-processing approach includes image contrast enhancement and image stitching in a coarse-to-fine manner, which are employed to improve the quality of raw images and to avoid repeating detection for overlapped regions of the captured tunnel images respectively. To achieve automatic tunnel surface defect detection with high precision, we propose a multi-layer feature fusion network, based on the Faster Region-based Convolutional Neural Network (Faster RCNN). Our image pre-processing and the defect detection methods also promising performance in terms of recall and precision, which is demonstrated through a series of practical experimental results. Moreover, our MTSIS has been successfully applied on several metro lines.