黑龙江科技大学学报2026，Vol.36Issue(3)：439-444,6.DOI:10.3969/j.issn.2095-7262.2026.03.016
改进YOLOv11的轻量级钢板表面缺陷检测算法
Algorithm of surface defect detection of lightweight steel plate based on improved YOLOv11
摘要
Abstract
This paper aims to address the issues of missed detection,high false detection rates,and deployment on the resource-constrained devices in industrial steel plate surface defect detection,and pro-poses a lightweight defect detection algorithm based on an improved YOLOv11.The study consists of tak-ing PP-LCNet with added residual structure as the backbone feature network of the target detection net-work for the reduction of the complexity of model operation and the number of parameters;performing the multi-scale feature fusion by BiFPN to improve the detection ability of objects of different scales;and in-troducing improved SimAM attention mechanism module to enhance the feature extraction capability for better focus on feature image regions.The simulation and experimental results show that the improved model achieves a detection accuracy of 77.1%on the NEU-DET dataset,which is 2.7%higher than that of the YOLOv11n model with the parameters of 2.1 M and a 19.2%reduction compared to the YOLOv11n model.This model becomes more lightweight while enhancing recognition accuracy.关键词
缺陷检测/YOLOv11算法/PP-LCNet/SimAM注意力机制Key words
defect detection/YOLOv11 algorithm/PP-LCNet/SimAM attention mechanism分类
信息技术与安全科学引用本文复制引用
李忠勤,寇贞珍..改进YOLOv11的轻量级钢板表面缺陷检测算法[J].黑龙江科技大学学报,2026,36(3):439-444,6.基金项目
国家自然科学基金项目(62441306) （62441306）
黑龙江省省属高等学校基本科研业务费项目(2025-KYYWF-ZR0593) （2025-KYYWF-ZR0593）
