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| 基于迁移学习和模型融合的粗糙度检测系统设计 |
| Design of roughness detection system based on transfer learning and model fusion |
| 投稿时间：2022-11-10 |
| DOI：10.3969/j.issn.1005-5630.2023.004.003 |
| 中文关键词: 迁移学习 模型融合 粗糙度检测 |
| 英文关键词:transfer learning model fusion roughness detection |
| 基金项目: |
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| 中文摘要: |
| 针对传统粗糙度测量方法识别准确率不高的问题，提出了基于迁移学习和模型融合的粗糙度检测方法。首先，采用所设计粗糙度检测系统中的CCD相机模组采集工件表面图像并制作数据集；其次，通过迁移微调VGGNet-19、Inception-V3 以及DenseNet121进行多模型融合，得到了适用的粗糙度检测模型；最后，用数据集进行网络训练以提取图像中的纹理细节特征，实现对粗糙度等级的精准识别。针对车削、铣削和磨削共15种粗糙度等级图像进行实验验证，系统识别精度可达91%。结果表明，所提出的系统可有效地实现粗糙度等级自动检测。 |
| 英文摘要: |
| According to the problem of low recognition accuracy of traditional roughness measurement methods, a roughness detection method based on transfer learning and model fusion was proposed. Firstly, the CCD module in the roughness detection system was used to collect the workpiece surface images and construct a data set. Secondly, through the migration fine-tuning VGGNet-19, Inception-V3 and DenseNet121 multi-model fusion, a suitable roughness detection model is obtained by multi-model fusion. Finally, the data set is used for network training to extract the texture details from the images and achieve accurate recognition of the roughness level. The experimental results show that 15 different roughness level images from turning, milling and grinding are used, and the recognition accuracy of the system can reach up to 91%. The results show that the proposed system can effectively realize the automatic detection of roughness grade. |
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