[1]CHEN Guihui,HE Long,LI Zhongbing,et al.Chip resistance recognition based on convolution neural network[J].CAAI Transactions on Intelligent Systems,2019,14(2):263-272.[doi:10.11992/tis.201710005]
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Chip resistance recognition based on convolution neural network
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 2
Page number:
263-272
Column:
学术论文—机器学习
Public date:
2019-03-05
- Title:
- Chip resistance recognition based on convolution neural network
- Keywords:
- Chip resistance recognition; convolution neural network; AlexNet model; GoogLeNet model; ResNet model
- CLC:
- TP391
- DOI:
- 10.11992/tis.201710005
- Abstract:
- Chip resistors are widely used in intelligent electronic devices because of their unique properties such as small size and stable performance. The chip resistors produced by the factory must be identified for defects in both front and back faces, polarity, and type in order to guarantee the quality. However, such identification largely relies on the eye detection, which is inefficient, prone to error, and costly. In this paper, considering the limitation of the traditional image recognition methods and the great achievements of convolutional neural network (CNN) in image recognition in recent years, three CNN models, AlexNet model, GoogLeNet model, and ResNet model, with appropriate depth and training parameters of about 4M (million) are designed to overcome the demerits of low speed that results in the inability to meet the real-time requirement. These models overcome the low accuracy problem of generalization recognition associated with the prevailing CNN models, which is caused by many trainable parameters and many layers of model. Experiments show that the recognition accuracy of these three models exceeds 90%. The highest recognition accuracy rate is 95%, and the recognition speed is 0.203 s/piece (256×256 pixels, CORE I5). Therefore, these three CNN models can be adopted in practice and have a strong feasibility and replicability; thus, they have a great potential to improve the production efficiency and product quality for chip resistors.
- References:
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Last Update:
2019-04-25