科技创新与应用2024，Vol.14Issue(11)：168-172,5.DOI:10.19981/j.CN23-1581/G3.2024.11.040
基于正则化YOLO的钢表面缺陷检测方法
摘要
Abstract
Steel surfaces often display intricate texture patterns that are similar to defects,posing a challenge to accurately i-dentify actual defects.In this study,a steel surface defect detection method based on the regularised YOLO framework is proposed based on the baseline model YOLOv8s.Firstly,coordinate attention(CA)is embedded in the C2F framework to enhance the fea-ture extraction capability of the backbone network using a lightweight attention module.Secondly,the neck design employs de-formable convolution(DCN)to weight the fusion of multi-scale feature maps to enhance the feature fusion capability.Finally,the loss function of the model is regularised to improve the generalisation performance of the model.The model achieves 77.94%mAP0.5 on the NEU-DET dataset.a 2.39%improvement over the baseline model.The method proved to be more suitable for in-dustrial inspection.关键词
YOLOv8s/钢表面缺陷检测/CA/DCN/正则化Key words
YOLOv8s/Steel Surface Defect Detection/CA/DCN/regularization分类
矿业与冶金引用本文复制引用
邓焕..基于正则化YOLO的钢表面缺陷检测方法[J].科技创新与应用,2024,14(11):168-172,5.
