Scratch Detection on the Appearance of Smart Meter HPLC Communication Module Based on Deep Learning
Aiming at the problem of the appearance scratch detection of the smart meter HPLC communication module, an advanced pixel-level crack detection architecture Res-DU-Net is proposed, which is characterized by the use of deep convolutional neural network technology. First, Res-DU-Net achieves pixel-lev...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | Chinese |
| Published: |
Harbin University of Science and Technology Publications
2022-06-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2097 |
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| Summary: | Aiming at the problem of the appearance scratch detection of the smart meter HPLC communication module, an advanced pixel-level crack detection architecture Res-DU-Net is proposed, which is characterized by the use of deep convolutional neural network technology. First, Res-DU-Net achieves pixel-level slip detection through operations such as convolution, residual structure, cavity convolution, and cascade, forming a U-shaped model structure. Secondly, the Adam algorithm was used to train and verify the appearance image of the smart meter communication module. Finally, Res-DU-Net has an accuracy rate of 0.985 and a recall rate of 0.987 when the learning rate is 0.001 and the loss rate is 0.020. Experimental results prove that Res-DU-Net has more advantages than traditional methods, Fully Convolutional Network (FCN) and U-Net in pixel-level scratch detection. |
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| ISSN: | 1007-2683 |