基于双网络的钢材表面缺陷检测方法

Steel Surface Defect Detection Method Based on Double Network

针对传统算法检测钢材表面缺陷(如开裂、斑块、划痕等)精准度较低的问题,提出一种基于分割与分类的两段式深度学习网络。该网络是专为表面缺陷的检测、分割以及分类而设计的。第一阶段利用YOLOv5算法对钢材表面的缺陷进行定位、分割;第二阶段使用EfficientNet网络对钢材表面的六种缺陷类型进行分类。实验结果表明,相较于传统的YOLOv5算法,该方法的平均精准度提高了16%,适合用于钢材表面缺陷检测。

In order to solve the problem of low accuracy of traditional algorithms for detecting steel surface defects such as cracks,patches and scratches,a two-stage deep learning network based on segmentation and classification is proposed in this paper,the network is designed for the detection,segmentation and classification of surface defects.In the first stage,uses YOLOv5 algorithm to locate and segment the defect on the steel surface.In the second stage,uses the EfficientNet network to classify six types of defects on steel surfaces.The experimental results show that the average accuracy of this method is improved by 16%compared with the traditional YOLOv5algorithm,and it is suitable for steel surface defect detection.