计算技术与自动化2024，Vol.43Issue(2)：10-16,7.DOI:10.16339/j.cnki.jsjsyzdh.202402002
基于改进YOLOv7的钢材表面缺陷检测
Steel Surface Defect Detection Based on Improved YOLOv7
摘要
Abstract
Surface defects on steel are a significant challenge for the steel industry.Traditional steel defect detection methods suffer from low efficiency and accuracy.To address these issues,an AFSD-YOLOv7 model has been designed for real-time steel surface defect detection.First,a lightweight convolutional structure was used to replace the standard convolu-tional structure in the YOLOv7 model,speeding up the inference process.Next,a fast spatial pyramid pooling structure was used to replace the original spatial pyramid pooling structure to accelerate the network's feature extraction process.Finally,an improved ECA-Net attention mechanism was added to enhance the model′s detection accuracy.Experimental results show that AFSD-YOLOv7 can effectively identify steel defects.Compared to the YOLOv7 model,AFSD-YOLOv7 reduces compu-tation by 54.8%and improves mAP by 3.2%,indicating significant practical value for steel surface defect detection.关键词
钢材/缺陷检测/YOLOv7/神经网络/深度学习/注意力机制/标准卷积Key words
steel/defect detection/YOLOv7/neural networks/deep learning/attention mechanism/standard convolu-tion分类
信息技术与安全科学引用本文复制引用
付帅,凌铭,楚东港..基于改进YOLOv7的钢材表面缺陷检测[J].计算技术与自动化,2024,43(2):10-16,7.基金项目
上海市技术标准项目(21DZ2204300) （21DZ2204300）
