Abstract：The surface defects produced during the processing of automobile wheels seriously affect the aesthetics and service performance of the whole vehicle. Aiming at the problems of low manual defect detection efficiency and high missed detection rate at this stage, a method for detecting wheel surface defects based on the improved YOLOv4 algorithm is proposed. Constructed a wheel defect data set, including six types of surface defects, composed of 2346 images of 4928×3264pixel, using K-means method for priori box clustering, and focusing on YOLOv4 algorithm on small-scale defects such as fiber and sticky aluminum insufficient detection accuracy. In the Neck part of the original network, a thinned U-shaped network module (TUM) and attention mechanism are introduced to enhance effective features and suppress invalid features, strengthen multi-scale feature extraction and fusion, and improve the possibility of feature processing small target information loss problem; based on self-built data sets, training and testing the defect detection performance of different algorithms and verify the effectiveness of the improved modules, the results show that the average defect detection accuracy of the method reaches 85.8%, and it greatly improves small-size defects such as aluminum sticking. The detection ability of, the detection accuracy is the highest among many comparison algorithms.
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2022, Vol. 43 
