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Ultrasonic Detection and Classification for Internal Defect of Rail Based on Deep Learning

被引：0
|
作者：
Hu, Wenbo [1 ,2 ,3 ]
Qiu, Shi [1 ,2 ,3 ]
Xu, Xinyue [1 ,2 ,3 ]
Wei, Xiao [1 ,2 ,3 ]
Wang, Weidong [1 ,2 ,3 ]
机构：
[1] School of Civil Engineering, Central South University, Changsha,410075, China
[2] Moe Key Laboratory of Engineering Structures of Heavy-Haul Railway, Changsha,410075, China
[3] Intelligent Monitoring Research Center of Rail Transit Infrastructure, Central South University, Changsha,410075, China
来源：
Tiedao Xuebao/Journal of the China Railway Society | 2021年 / 43卷 / 04期
关键词：
The real-time detection and treatment of internal defects of the rail will effectively reduce the risk of accidents. Compared with manual physical detection method that is time-consuming and laborious; ultrasonic detection technology can detect the internal state of the rail in real time; but the identification of defects still relies on manual or image processing technology to process one by one; which is likely to cause missed or wrong detection. This paper proposed a new deep learning-based ultrasonic testing data post-processing method for rail internal defect to realize the automation of defect identification and classification; which is verified and evaluated from three aspects: data; method and interference factors. The results show that Resnet-50 deep residual network has a classification accuracy of 99.3% for the five types of labels; with F1 scores of 99.24% (head kernel defect); 98.5% (web oblique crack); 99% (base crescent crack); 99.75% (joint defect) and 100% (non-defect); respectively. In addition; with good robustness to clutter interference; it is superior to three traditional machine learning methods; ensuring the real-time; accurate and efficient detection and treatment of internal damage to the rail. © 2021; Department of Journal of the China Railway Society. All right reserved;
D O I：
10.3969/j.issn.1001-8360.2021.04.014
中图分类号：
学科分类号：
摘要：
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页码：108 / 116
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