河北科技大学学报2025，Vol.46Issue(5)：521-532,12.DOI:10.7535/hbkd.2025yx05005
基于YOLO11n-SRA的带钢表面缺陷检测
Strip steel surface defect detection based on YOLO11n-SRA
摘要
Abstract
To address the problem of low detection accuracy due to complex target-background interactions and insufficient feature processing capability of existing algorithms in steel surface defect detection,an improved detection algorithm YOLO11n-SRA was proposed.Firstly,the SHSA attention mechanism was introduced to replace the PSA attention mechanism in the C2PSA module in order to improve the detection efficiency and accuracy of small targets.Secondly,in the neck network,the RCM module was embedded into the C3k2 module,utilizing its context capturing and feature enhancement capabilities to improve multi-scale detection performance.Thirdly,the ATFL loss function was introduced to effectively alleviate the imbalance between the target and background in defect images in order to enhance the stability of the model training process.Finally,experimental verification was conducted on the NEU-DET and GC10-DET datasets.The contrast experiment and generalization experiment results show that compared to the YOLO11n algorithm,YOLO11n-SRA achieves a 3.4 and 1.6 increase(in percent)in mAP,respectively,45.8 and 20.6 frame/s increase in FPS,respectively,and 5.1 and 4 increase in percent in recall rate,respectively,with no change in parameter count or computational cost.The improved algorithm strikes a good balance between detection accuracy and efficiency,which provides reference for its improvement and practical deployment.关键词
计算机图像处理/YOLO11n/注意力机制/缺陷检测/损失函数Key words
computer image processing/YOLO11n/attention mechanism/defect detection/loss function分类
信息技术与安全科学引用本文复制引用
钱俊磊,黄庆尧,曾凯,杜学强,王雁,王杏娟..基于YOLO11n-SRA的带钢表面缺陷检测[J].河北科技大学学报,2025,46(5):521-532,12.基金项目
国家自然科学基金(52374335) （52374335）
中央引导地方科技发展资金项目(236Z1017G) （236Z1017G）
唐山市市级科技计划项目(22130220G) （22130220G）
