基于机器视觉的轴承全表面缺陷在线检测方法
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1.四川轻化工大学机械工程学院 宜宾 644000;2.中国科学院成都计算机应用研究所 成都 610041

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TN919.8;TP391

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国家自然科学基金(52370067)、过程装备与控制工程四川省高校重点实验室开放基金(GK202209)、四川省重点实验室资助项目(NJ2018-05)资助


Online detection method for full surface defects of bearings based on machine vision
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1.School of Mechanical Engineering, Sichuan University of Science & Engneering,Yibin 644000, China;2.Chendu Institute of Computer Application, Chinese Academy of Sciences,Chengdu 610041, China

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    摘要:

    为解决当前轴承缺陷图像采集难、检测慢等问题,搭建一种基于机器视觉的单工位轴承全表面缺陷在线检测系统。系统利用线扫描相机结合光学系统进行高效采集轴承全表面图像。所搭建的缺陷检测网络模型以ConvNext为特征提取网络,利用特征融合补充特征信息;再进行轻量化改进;同时采用多任务学习的训练方法,使模型具有针对性处理各层次特征信息的能力,最终显著提升模型在轴承表面缺陷检测任务上的表现。实验表明,相比ConvNeXt-Tiny来说,所搭建深度学习模型的检测精度模型提高了4.14%,检测精度达90.02%。单工位轴承全表面缺陷在线检测系统依靠CPU进行计算时,平均检测时间为每个轴承0.735 s。系统体积小、成本低,同时满足轴承表面缺陷在线检测的要求,具有良好的应用前景。

    Abstract:

    To address the current challenges of difficult image acquisition and slow detection for bearing defects, a single-station online detection system for full surface defects of bearings based on machine vision has been developed. The system utilizes a line-scan camera in conjunction with an optical system to efficiently capture images of the entire surface of the bearings. The defect detection network model constructed is based on ConvNeXt as the feature extraction backbone, employing feature fusion to supplement feature information, it then undergoes lightweight modifications. Additionally, a multi-task learning training approach is adopted, enabling the model to effectively process feature information at various levels, thereby significantly enhancing its performance in bearing surface defect detection tasks. Experimental results show that compared to ConvNeXt-Tiny, the proposed deep learning model achieves a 4.14% improvement in detection accuracy, reaching 99.02%. When relying on CPU for computation, the average detection time per bearing by the single-station full surface defect online detection system is 0.735 seconds. This system is characterized by its compact size, low cost, and ability to meet the requirements for online detection of bearing surface defects, showcasing promising application prospects.

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蔡炳彬,黄丹平,张浩田,廖世鹏.基于机器视觉的轴承全表面缺陷在线检测方法[J].电子测量技术,2025,48(9):100-110

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  • 在线发布日期: 2025-05-23
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