Defect detection and recognition technology in wheel images based on deep learning

Yanqing Zhang; Xiaorong Gao; Jianping Peng; Jianqiang Guo

doi:10.1117/12.3006110

24 November 2023 Defect detection and recognition technology in wheel images based on deep learning

Yanqing Zhang, Xiaorong Gao, Jianping Peng, Jianqiang Guo

Proceedings Volume 12935, Fourteenth International Conference on Information Optics and Photonics (CIOP 2023); 129351N (2023) https://doi.org/10.1117/12.3006110
Event: Fourteenth International Conference on Information Optics and Photonics (CIOP 2023), 2023, Xi’an, China

Abstract

The ultrasonic detection technology of railway locomotive wheels is of great significance to the safety of train operation. However, the current detection technology relies on experts ' manual operation, which has the disadvantages of low accuracy and high cost. In this paper, a detection method based on improved Exceeding YOLO Series in 2021 (YOLOX) is proposed. Firstly, a series of processing such as cutting and rotating the ultrasonic B-scan image obtained by LU system is carried out to obtain the B-scan data set of wheel ultrasonic defects after data enhancement. Secondly, we add an adaptive spatial feature fusion block (ASFF) to the tail of the Neck module of the YOLOX detection algorithm, and further improve the multi-scale feature map. Finally, the original BCEWithLogitsLoss in the loss function is replaced by FocalLoss to improve the ability to distinguish defects from similar backgrounds. The test results show that the detection rate of each type of the improved YOLOX model is more than 90 %, the false negative rate and false positive rate are less than 10 %, and the detection speed is 17 ms. Compared with the original YOLOX network and other mainstream detection models, the improved YOLOX model has the best detection performance. This study provides a new idea for the automation of ultrasonic testing of train wheels.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanqing Zhang, Xiaorong Gao, Jianping Peng, and Jianqiang Guo "Defect detection and recognition technology in wheel images based on deep learning", Proc. SPIE 12935, Fourteenth International Conference on Information Optics and Photonics (CIOP 2023), 129351N (24 November 2023); https://doi.org/10.1117/12.3006110

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KEYWORDS

Defect detection

Ultrasonics

Object detection

Feature fusion

Feature extraction

Performance modeling

Data modeling

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