A Method of Locating and Measuring Train Wheel Tread Defects Based on YOLOv3-tiny

Abstract

Aiming at the problem of insufficient detection frame size accuracy in the deep learning based wheelset tread defect detection algorithm, this paper proposes a tread defect detection algorithm based on YOLOv3-tiny and traditional image algorithm, which can realize fast positioning of defects with low CPU consumption and precise measurement of geometric parameters. First, image enhancement is performed on the small samples obtained in the industrial scene, and then the YOLOv3-tiny algorithm is used to perform migration learning on tread defects to achieve rough localization of defects. In order to solve the key problems that the detection frame is too large and too small, traditional image algorithms such as Fourier transform, band-stop filter, and threshold segmentation are used to construct a defect size measurement model, contours of roughly located defects are extracted and detection frame size is optimized, and finally location and size of defect are accurately calculated. Defect location experimental results show that the average accuracy of defect identification is 89.4% and the CPU consumption does not exceed 10% when IoU threshold is 0.5. Experimental results of defect measurement show that the algorithm can optimize 74 out of 90 inspection frames and obtain a more accurate defect size. The above experimental results show that the detection algorithm in this paper is effective in improving size accuracy of detection frame.

Keywords

• tread defect
• object detection
• yolov3-tiny
• fourier transform
• deep learning