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.

由于周围环境变化，火车引起的振动和人为干扰的影响，经常会损坏地铁隧道表面。因此，由于缺乏适当和有效的维护，隧道表面损坏引起的事故随时可能发生。据我们所知，有效的维护很大程度上取决于全面而准确的缺陷检查，由于恶劣的环境（例如，照明不足，检查的时间窗口有限等），这是一项艰巨的任务。为了解决这些问题，我们设计了一个自动的地铁隧道表面检测系统（MTSIS），以进行高效，准确的缺陷检测，其中包括硬件和软件部件的设计。对于硬件组件，我们设计了一种数据收集系统，可以高速捕获高分辨率的隧道表面图像。对于软件部分，我们提出了一种隧道表面图像预处理方法和一种缺陷检测方法，以高精度识别缺陷。图像预处理方法包括以粗糙到精细的方式进行图像对比度增强和图像拼接，以提高原始图像的质量并避免分别对捕获的隧道图像的重叠区域进行重复检测。为了实现高精度的隧道表面缺陷自动检测，我们提出了一种基于快速区域的卷积神经网络（Faster RCNN）的多层特征融合网络。我们的图像预处理和缺陷检测方法在召回率和精度，通过一系列实际实验结果证明了这一点。此外，我们的MTSIS已成功应用于多个地铁线路。