Ultrasonic defect detection of railway wheels based on deep learning

Ultrasonic testing is a highly efficient NDT technique, which is widely used and rapidly developing among the many NDT methods. With the rapid development of high-speed railways, existing testing speeds are often unable to meet demand. Wheel integrity is essential for safety. Existing ultrasonic testing techniques now rely more on the intuition and experience of skilled workers, which often results in a high rate of missed inspections. In this paper, images of wheels with different defect depths are firstly annotated and calibrated. Secondly, the structure of the YOLO v5 algorithm is improved for the characteristics of ultrasonic defects, making it more efficient for recognizing B-scan defect images of wheels. Finally, the data is validated and tested for each category. The experimental results show that the improved algorithm has significantly enhanced the performance of wheel B-scan defect recognition, reducing the false alarm rate while stabilizing the detection speed of one wheel within 10s.