Rail fastener defect inspection method for multi railways based on machine vision  

作　　者：Junbo Liu YaPing Huang ShengChun Wang XinXin Zhao Qi Zou XingYuan Zhang 

机构地区：[1]Infrastructure Inspection Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing,China [2]Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing Jiaotong University,Beijing,China [3]Beijing R&D Centre,Huawei Technologies Co Ltd,Shenzhen,China

出　　处：《Railway Sciences》2022年第2期210-223,共14页铁道科学（英文）

基　　金：funded by the Key Research and Development Project of China Academy of Railway Sciences Corporation Limited(2021YJ310).

摘　　要：Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.

关 键 词：Rail fastener Defects inspection Multi railways Image recognition Deep convolutional neural network Machine vision 

分 类 号：U21[交通运输工程—道路与铁道工程]