黑龙江科技大学学报2024，Vol.34Issue(6)：990-996,7.DOI:10.3969/j.issn.2095-7262.2024.06.026
一种改进YOLOv7的钢铁表面缺陷检测优化模型
An optimized model for detecting steel surface defect based on improved YOLOv7
摘要
Abstract
This paper is aimed at meeting the urgent demand for high real-time and accuracy in in-dustrial steel surface defect detection and proposes ansteel surface defect detection algorithm based on YOLOv7 improved.The study firstly introduces K-means++algorithm clustering analysis for the anchor frame adapting to all defect types in the dataset;and introducing the SENet,CBAM,ECANet,and CA attention mechanisms,respectively,for improving the model′s attention to the target information at the same time.The results show that in the NEU-DET dataset,the improved four algorithms have higher de-tection accuracy compared with the original YOLOv7 algorithm.The YOLOv7+CBAM algorithm is most effective,with an increase of 1.64%in the detection accuracy relative to the YOLOv7 algorithm,and an increase of 8.59%in the fine detection accuracy of crack defects.Compared with the previous steel sur-face defect detection algorithm,the improved algorithm achieves significant improvement in performance,with a detection speed of 32 M and a detection accuracy of 80.79%,as which accurately detects the steel surface defects while keeping the original detection speed basically unchanged.关键词
缺陷检测/YOLOv7算法/K-means++/CBAM注意力机制Key words
defect detection/YOLOv7 algorithm/K-means++/CBAM attention mechanism分类
信息技术与安全科学引用本文复制引用
史健婷,李洋..一种改进YOLOv7的钢铁表面缺陷检测优化模型[J].黑龙江科技大学学报,2024,34(6):990-996,7.基金项目
黑龙江省属高校基本科研业务费项目(2023-KYYWF-0547) （2023-KYYWF-0547）
