工业技术与职业教育2025，Vol.23Issue(2)：13-18,6.
一种冷轧带钢表面缺陷检测模型
A Surface Defect Detection Model for Cold Rolled Strip Steel
摘要
Abstract
As an important raw material,strip steel has been applied in various industries,and its quality directly affects the performance and quality of the final product.In order to detect the surface defects of strip steel effectively,and control the strip steel surface quality,a detection model is proposed in this paper.The model has been improved under the YOLOv5 framework,which has three main improvements:(1)Enhancing the feature extraction architecture by introducing an attention mechanism module;(2)Optimizing the model training process by using the SIOU(Sum of Intersection over Union)loss function;(3)Improving the loss function of confidence prediction to enhance the accuracy of the model in identifying real objects.The experimental results show that the proposed model can detect the surface defects in cold-rolled strip steel effectively,and the average detection accuracy of the proposed model has been improved,comparing with the similar algorithms YOLOv4 and YOLOv5.关键词
带钢表面缺陷/YOLOv5/注意力机制/SIOU损失函数/置信度预测损失函数Key words
surface defects of strip steel/YOLOv5/attention mechanism/SIOU loss function/confidence prediction loss function分类
信息技术与安全科学引用本文复制引用
曾艳,吴泽启,张天有,李佳瑶,石惠文..一种冷轧带钢表面缺陷检测模型[J].工业技术与职业教育,2025,23(2):13-18,6.基金项目
教育部高等学校科学研究发展中心项目"激光切割钢板智能码垛机器人研究"(课题编号:2023DT020),主持人吴泽启. （课题编号:2023DT020）
