机械科学与技术2025，Vol.44Issue(10)：1785-1792,8.DOI:10.13433/j.cnki.1003-8728.20230342
改进YOLOv5s的轻量化轴承缺陷检测方法
Lightweight Bearing Defect Detection Method Based on Improved YOLOv5s Feature Extraction Network
摘要
Abstract
Aiming at the problem of low accuracy and many parameters of the existing bearing defect detection models,a lightweight bearing defect detection method based on YOLOv5s is proposed in this paper.Firstly,the improved EfficientViT-B0 is used to reconstruct the YOLOv5s feature extraction network,which can reduce the computational complexity of the model and extract deeper semantic feature information.Secondly,in order to solve the problem of difficult and easy sample imbalance,an F-CIoU is designed as a loss function to improve the positioning regression accuracy and robustness of the detection frame.Finally,a Dynamic Head(DyHead)based on multiple attention mechanisms is used to strengthen the feature semantic information and further optimize the classification and location of bearing surface damage.The experimental results show that map@0.5 of the improved YOLOv5s is 93.8%,the parameter amount and calculation amount are reduced by 42.3%and 51.9%respectively.The algorithm meets the lightweight requirements of industrial inspection deployment while maintaining high accuracy.关键词
EfficientViT-B0/动态头/F-CIoU/缺陷检测/轴承Key words
EfficientViT-B0/Dynamic head/F-CIoU/defect detection/bearing分类
矿业与冶金引用本文复制引用
彭晏飞,李冬雪,陈曦涛..改进YOLOv5s的轻量化轴承缺陷检测方法[J].机械科学与技术,2025,44(10):1785-1792,8.基金项目
国家自然科学基金项目(61772249)、辽宁省高等学校基本科研项目(LJKZ0358)及辽宁工程技术大学双一流学科创新团队项目(LNTU20TD-27) （61772249）
