Defect Detection on Wire Based on YOLOv8

The false detection rate and missed detection rate of wire defects using manual detection is unstable, when automatic winding machines are applied. Aiming at addressing the problem, a defect detection model based on YOLOv8 named Wire-YOLOv8 was proposed. First of all, SPD-Conv module is used to replace Conv module in the backbone to help extracting multi-scale features and obtain features from small defects, in this way, the ability to extract features is enhanced, detection accuracy is improved and miss detection rate drops. Furthermore, SimSPPF take the place of SPPF to reduce the size of computation and improve detection speed. Finally, WIoU is introduced to make loss function work better, optimizing localization ability of the model, providing high quality anchor and strong ability to detect defects. The experimental results show that the average accuracy ([email protected]) of the improved model Wire-YOLOv8 for the grease dirt, outer insulation defect and inner insulation defect of wire reaches 92.7%, 94.6% and 97.3%, which is 1.5%, 2.3% and 1.9% higher than the original model, respectively, and the detection speed reaches 211FPS, which can serve as a compelling solution to detect wire defects in industrial sites.