Rail Internal Defect Detection Method Based on Enhanced Network Structure and Module Design Using Ultrasonic Images

作     者：Fupei Wu Xiaoyang Xie Weilin Ye Fupei Wu;Xiaoyang Xie;Weilin Ye

作者机构：Department of Mechanical EngineeringCollege of EngineeringShantou UniversityShantou 515063China 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2023年第36卷第6期

页      面：277-288页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 1010[医学-医学技术（可授医学、理学学位）] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0814[工学-土木工程] 1009[医学-特种医学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by National Natural Science Foundation of China(Grant No.61573233) Guangdong Provincial Natural Science Foundation of China(Grant No.2021A1515010661) Guangdong Provincial Special Projects in Key Fields of Colleges and Universities of China(Grant No.2020ZDZX2005). 

主　　题：Ultrasonic detection Rail defects detection Deep learning Enhanced network structure Module design 

摘      要：Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.