| 谷峥岩,魏利胜.基于深度学习网络的轴承工件自动检测[J].电子测量与仪器学报,2021,35(9):80-88 |
| 基于深度学习网络的轴承工件自动检测 |
| Automatic detection for bearing roller based on deep learning network |
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| DOI： |
| 中文关键词: 轴承滚子 数据增强 深度学习模型 语义分割 迁移学习 |
| 英文关键词:bearing roller data enhancement deep learning model semantic segmentation transfer learning |
| 基金项目:安徽省教育厅重大项目(KJ2020ZD39）、安徽省检测技术与节能装置重点实验室开放基金项目(DTESD2020A02)资助 |
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| 摘要点击次数: 629 |
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| 中文摘要: |
| 针对实际生产中轴承滚子原始故障数据量少,数据集不平衡的问题,提出一种数据增强策略对原始的数据集进行扩充,
并结合 U-Net 框架和轻量级深度学习模型构建了一个端到端的轴承滚子语义分割模型方法。 通过结合 U-Net 框架和轻量级深
度学习模型 MobileNetV1、DenseNet121 构建了端到端的轴承滚子语义分割模型 LS-MobileNetV1、LS-DenseNet121,将所提模型基
于迁移学习策略进行了训练,与其他模型进行对比实验分析。 结果表明,与现有方法相比,本文方法在具有更少参数量的情况
下实现了更高的分割精度与更具鲁棒性的检测效果,验证了所提方法的有效性。 |
| 英文摘要: |
| Aiming at the problem of small amount of original fault data and unbalanced data set of bearing rollers in actual production, a
data enhancement strategy was proposed to expand the original bearing image data set, and combined with the U-Net framework and
lightweight deep learning model to construct an end-to-end bearing roller semantic segmentation model method. By combining the U-Net
framework and lightweight deep learning models MobileNetV1 and DenseNet121, the end-to-end bearing roller semantic segmentation
models LS-MobileNetV1 and LS-DenseNet121 are constructed,the proposed models are trained based on the transfer learning strategy,
and compared with other models for experimental analysis. The results show that compared with the existing methods, the method in this
paper achieves higher segmentation accuracy and more robust detection results with few parameters, which verifies the effectiveness of the
proposed method. |
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