Research on Defect Detection of Railway Key Components Based on Deep Learning

被引：0

作者：

Zhao B. ^{[1
,2
]}

Dai M. ^{[2
]}

Li P. ^{[2
]}

Ma X. ^{[2
]}

Wu Y. ^{[2
]}

机构：

[1] Department of Postgraduates, China Academy of Railway Sciences, Beijing

[2] Railway Big Data Research and Application Innovation Center, China Academy of Railway Sciences, Beijing

来源：

Tiedao Xuebao/Journal of the China Railway Society | 2019年 / 41卷 / 08期

关键词：

Convolutional neural network; Defect detection; Detect classification; Image super resolution; Object detection;

D O I：

10.3969/j.issn.1001-8360.2019.08.009

中图分类号：

学科分类号：

摘要：

Automatic detection of defect images of key components is of great significance for the operation and maintenance of the Fuxing Electric Multiple Units.However, the current reliance on manual analysis of detection images by professional personnel consumes manpower and resources, resulting in a long detection cycle and uncertain detection accuracy.In this paper, a framework which combines component detection and defect classification, called multi-channel defect detection framework(MCDDF), was proposed.The component detection channel based on object detection algorithm can locate key components of EMU.The located components were clipped for super-resolution enhancement, then were sent to detect classification channel to realize accurate classification of defect categories based on transfer learning method.In the experiments, the performance improvement of two channels was analyzed respectively.The performances of MCDDF and traditional object detection methods on railway key components defect detection task were compared, where the effectiveness of the MCDDF method was verified. © 2019, Department of Journal of the China Railway Society. All right reserved.

引用

页码：67 / 73

页数：6

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