矿业科学学报2026，Vol.11Issue(1)：218-227,10.DOI:10.19606/j.cnki.jmst.2025072
基于改进YOLOv8n的带钢表面缺陷检测方法
Improved YOLOv8n-based method for surface defect detection on strip steel
摘要
Abstract
Traditional methods for strip steel surface defect detection often suffer from low efficiency and poor accuracy in complex industrial environments.This study therefore proposed a strip surface defect detection method based on YOLOv8n.The present YOLOv8n model is limited in its accuracy of strip surface defect detection for its loss of local information due to global convolution,insufficient diversity in extracted features and easy overfitting during training.To address this,the following improvements are introduced:① A Clo-conv Net module is first proposed,which uses contextual local enhancement mechanism to strengthen the local features and improve the model's ability to capture local informa-tion.② We put forward the attention mechanism of frequency coordinate FC-CA.It integrates feature frequency with position information to enrich feature diversity by improving the structure of coordinate attention CA.③ A confidence penalty is introduced,and the output is smoothed so that rapid conver-gence can be achieved after the model has learned a strong representation.Results show that the model accuracy was significantly improved as it was validated on small and blurred samples.The average pre-cision(0.5)rate improved by 8.9%,7.2%,and 6.2%respectively compared with the baseline YOLOv8n model while maintaining competitive inference speed.关键词
带钢表面缺陷/深度学习/局部增强/频率注意力/置信度惩罚Key words
strip surface defects/deep learning/local enhancement/frequency attention/confidence penalty分类
通用工业技术引用本文复制引用
陈磊,梁海月,周旭荣,徐传远,张静茹,姜丽..基于改进YOLOv8n的带钢表面缺陷检测方法[J].矿业科学学报,2026,11(1):218-227,10.基金项目
国家自然科学基金(51804310,52174095) （51804310,52174095）
