现代电子技术2024，Vol.47Issue(19)：115-122,8.DOI:10.16652/j.issn.1004-373x.2024.19.018
改进YOLOv8的轻量化轴承缺陷检测算法
Lightweight bearing defect detection algorithm combined with improved YOLOv8
摘要
Abstract
In view of the low accuracy and the large number of model parameters of the surface defect detection algorithms for industrial bearings,a lightweight object detection algorithm(MFA-YOLOv8)combined with improved YOLOv8 is proposed.A lightweight multi-scale feature convolution module EMFC(efficient multi-scale feature convolution)is designed,based on which the Bottleneck structure in the partial C2f of the trunk and neck is reconstructed to maintain lightweight while also capturing the detailed features of the information with different scales effectively.The focal modulation(FM)module is introduced to enhance the model´s ability to characterize the defective objects and the receptive field of the model.The attentional scale sequence fusion(ASF)module is introduced to further improve the model´s detection accuracy of bearing defects and reduce the quantity of the parameters.The experimental results show that the mean average precision mAP@0.5 of MFA-YOLOv8 is as high as 91.5%on the GGS dataset,which is 2.4%higher than that of the YOLOv8,and its number of parameters decreases by 21.9%,so the MFA-YOLOv8 can satisfy the requirements of bearing appearance defect detection in industrial sites.关键词
轴承表面缺陷检测/YOLOv8/多尺度特征卷积/焦点调制网络/注意力尺度序列融合/轻量化Key words
bearing surface defect detection/YOLOv8/multi-scale feature convolution/focal modulation network/ASF/lightweight分类
信息技术与安全科学引用本文复制引用
郎德宝,周凯红..改进YOLOv8的轻量化轴承缺陷检测算法[J].现代电子技术,2024,47(19):115-122,8.基金项目
国家自然科学基金面上项目(52075110) （52075110）
广西自然科学基金重点项目(2023GXNSFDA026045) （2023GXNSFDA026045）
