基于深度卷积神经网络的隧道衬砌裂缝识别算法

Tunnel Lining Crack Identif ication Algorithm Based on Deep Convolutional Neural Network

衬砌裂缝严重影响了铁路隧道的安全运营,采用机器视觉技术快速获取衬砌图片并进行裂缝识别是国内外的研究热点。衬砌裂缝图像信号具有复杂的特性,存在水渍、污染及其他结构缝等引起的噪声,加之光照不均匀、分布不规律等原因,使得传统的图像处理方法难以快速、准确地检测衬砌裂缝。本文提出一种基于深度神经网络的隧道衬砌裂缝识别算法,有效解决了裂缝识别速度慢、精度低等问题。分类结果精度达到94%,识别速度在GPU(Pascal Titan X)上每张图片仅需0.05 s;分割网络性能均交并比可达到65%,能够准确分割出裂缝形状。该算法具有很好的工程应用价值。

It is a research hotspot for railway to achieve rapid and accurate detection and identification of tunnel lining cracks, since tunnel lining cracks seriously affect the safety of railway tunnels.However,tunnel lining crack image signals have complex features.There are noises induced by waterlogging,pollution and other structural joints,in addition,uneven illumination and irregular distribution.Thus traditional image processing methods are difficult to achieve rapid and accurate detection and identification of tunnel lining cracks.An algorithm for tunnel lining crack identification based on deep convolution neural networks was proposed in this paper,which can effectively solve those problems above. The identification accuracy achieves 94%,and the identification speed is up to 0. 05 s per picture in the hardware environment of GPU（Pascal Titan X）.The segmentation performance achieves 65% MIOU（Mean Intersection Over Union）. The algorithm can accurately segment crack shape and has good engineering application value.