Abstract
Aiming at the traditional tire defect detection is difficult to meet the practical application requirements, a defect detection algorithm based on YOLOv8 is constructed. Firstly, the GAM attention mechanism is added to the backbone to improve the feature expression of tire defects and improve the detection efficiency. Secondly, the SPPF is replaced by SimSPPF to effectively capture multi-scale features. Finally, the CIoU loss function is replaced by Wise-IoU to improve the convergence ability of the model. The experimental results show that the constructed improved models P, mAP0.5 and mAP0.5:0.95 reach 86.4%, 88.2% and 46.9%, respectively, which are improved by 2.7%, 2.3% and 0.9% compared with the original YOLOv8n model. The algorithm can better improve the problems faced by traditional new energy vehicle tire defect detection.
Published Version
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