现代制造工程Issue(5)：126-134,9.DOI:10.16731/j.cnki.1671-3133.2025.05.016
改进YOLOv8n的电磁离合器端面缺陷检测
The electromagnetic clutch end face defect detection of improved YOLOv8n
摘要
Abstract
Electromagnetic clutch is an important part in the automobile production process.Aiming at the problems of small de-fect size of its adsorption surface,complex background texture and existing algorithms can not achieve the diversity of defects,a lightweight target detection algorithm based on improved YOLOv8n was proposed.EMA attention and partial convolution were in-tegrated in the backbone network,and a lightweight C2F-PE module was designed to improve the C2F structure and to enhance the feature extraction ability of the network.In order to promote richer feature fusion between the same scales,the Attention-based Intra-scale Feature Interaction(AIFI)module was introduced to replace the SPPF layer to capture more fine-grained information.A small object detection layer was added to the neck network,which effectively fused the shallow feature information and improved the model perception of small objects.The Slim-neck module was introduced to improve the neck network,which lightened the model while maintaining the detection accuracy of the network.The experimental results showed that compared with the YOLOv8n algorithm,the improved algorithm achieved an mAP@0.5 of 94.6％,which was an increase of 4.5％.The number of parameters was reduced by 13.3％,and the detection speed reached 81 f/s.The algorithm effectively balanced detection accu-racy and speed,meeting the needs for real-time detection in electromagnetic clutch production.关键词
YOLOv8n/电磁离合器/缺陷检测/轻量级网络/EMA注意力/内尺度特征交互/Slim-neck模块Key words
YOLOv8n/electromagnetic clutch/defect detection/lightweight network/EMA attention/intra-scale feature interaction/Slim-neck module分类
信息技术与安全科学引用本文复制引用
魏书豪,徐红伟,柯海森,李孝禄,丁建雄..改进YOLOv8n的电磁离合器端面缺陷检测[J].现代制造工程,2025,(5):126-134,9.基金项目
浙江省科技计划重点项目(2019C001128) （2019C001128）
