基于改进YOLOv5钢材表面缺陷检测技术

Improve YOLOv5 Steel Surface Defect Detection Technology

针对初始的YOLOv5目标检测算法对复杂目标中的缺陷特征提取不充分、定位不精确、检测精度低和漏检率高等问题,提出了改进的YOLOv5钢材表面缺陷检测算法。在锚框计算上,用K-Means++算法进行锚框选取,使得随机选取的聚类中心尽可能的趋于全局最优解,预测框更加精准。添加CBAM注意力机制,对复杂图像中的缺陷赋予更高的权重,增强对关键信息的关注度。通过实验对比后结果表明,改进后YOLOv5算法拥有更好的检测性能。

In response to the initial YOLOv5 target detection algorithm's problems of inadequate extraction of defect features in complex targets,imprecise localization,low detection accuracy and high miss detection rate,an improved YOLOv5 steel surface defect detection algorithm is proposed,in which the anchor frame is calculated with the K-Means++algorithm for anchor frame selection,so that the randomly selected clustering center tends to the global optimal solution as much as possible and the prediction frame is more accurate.The CBAM attention mecha-nism is added to give higher weights to the defects in complex images and enhance the attention to key information.The results after the experimental comparison show that the improved YOLOv5 algorithm has better detection per-formance.