基于改进YOLOv5s的滚动轴承表面缺陷识别算法  

Rolling Bearing Surface Defect Recognition Algorithm Based on Improved YOLOv5s

作　　者：宁少慧[1] 段攀龙 杜越 张少鹏 邓功也 NING Shaohui;DUAN Panlong;DU Yue;ZHANG Shaopeng;DENG Gongye(School of Mechanical Engineering,Taiyuan University of Science and Technology,Taiyuan Shanxi 030024,China)

机构地区：[1]太原科技大学机械工程学院,山西太原030024

出　　处：《机床与液压》2024年第18期230-236,共7页Machine Tool & Hydraulics

基　　金：山西省应用基础研究计划(20210302123212)。

摘　　要：为了解决机械设备轴承表面缺陷检测中多目标情形下的小目标漏检率高、检测速度慢、模型精度和特征提取泛化能力不足的问题,提出一种滚动轴承表面缺陷识别网络模型YOLOv5s-CDOD。在卷积操作前,使用B-ConvNeXt网络平衡模型的精度和复杂度,保留轴承表面小目标缺陷的特征,同时提升模型的泛化能力;通过将YOLOv5s网络中的传统卷积模块替换为具有二次深度过参数化卷积的卷积(DOD-Conv)模块,在不增加模型参数的情况下,提高模型的识别精度和速度;最后,在特征处理阶段,使用VariFocal Loss损失函数,增加模型对正样本目标的学习,对轴承小目标缺陷的检测精度进一步提升。实验结果表明:与原YOLOv5s网络相比,优化后的网络参数量减少了10%,使得模型的检测速度明显提升;同时,所提模型的平均检测精度达到了94%,对轴承表面小目标缺陷的识别率也有所提高。A surface defect recognition network model YOLOv5s-CDOD was proposed to address the issues of high small target miss rate,slow detection speed,insufficient model accuracy and feature extraction generalization ability in multi-object situations during bearing surface defect detection.The B-ConvNeXt network was employed before the convolution operation to balance the accuracy and complexity of model,preserving the features of small target defects on the bearing surface while improving the generalization ability of model.The traditional convolution modules in the YOLOv5s network were replaced with convolution modules(DOD-Conv)with two deep over-parameterized convolutions,to enhance the recognition accuracy and speed of the model without increasing the model parameters.Finally,VariFocal Loss function was used in the feature processing stage to increase the learning of model for positive sample targets,further improving the detection accuracy of small target defects on the bearing.The experimental results show that compared to the original YOLOv5s network,the optimized network reduces the parameter count by 10%,significantly improving the detection speed;moreover,the average detection accuracy of the proposed model reaches 94%,enhancing the recognition rate of small target defects on the bearing surface.

关 键 词：缺陷识别 YOLOv5s网络 B-ConvNeXt网络 二次深度过参数化卷积 VariFocal Loss函数 

分 类 号：TH165.3[机械工程—机械制造及自动化] TN911.7[电子电信—通信与信息系统]