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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 32, Number 12(3) (2020)
Copyright(C) MYU K.K.
pp. 4217-4231
S&M2406 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.3101
Published: December 22, 2020

Deep Learning Model for Determining Defects of Vision Inspection Machine Using Only a Few Samples [PDF]

Bo-lin Jian, Jui-Pin Hung, Cheng-Chi Wang, and Chun-Chang Liu

(Received August 2, 2020; Accepted November 13, 2020)

Keywords: deep learning, industrial machine vision, leakage, overkill, DarkNet-53

For the intelligent manufacturing field, after finishing the cutting process, a metal surface may have various defects such as scratches, residues, and dirt. However, the conventional method of determining defects has the disadvantages of being time-consuming and expensive. In addition, it is necessary to consider the cost of collecting samples and the labor cost when practically collecting samples from industries. Therefore, in this study, we optimized the determination of the defects of the production component by a deep learning (DL) model with a few samples and used an image sensor to take pictures of the specific area of the component. Meanwhile, an entropy calculation method is proposed to determine the most suitable kernel size of a convolution layer. We analyzed and established a deep learning model to determine whether the finished products of a vision inspection machine have defects using only a few samples. We compared the pros and cons of DarkNet-53, which is a convolutional neural network (CNN) that is 53 layers deep, and AlexNet, which is a deep CNN, with the DenseNet-201 model in the experiments. The obtained experimental results indicate that the proposed method can effectively increase the rate of recognition between defective and nondefective samples and reduce the training cost. The results of this paper may contribute to the development of a novel diagnostic technique and also be helpful for the intelligent manufacturing industry. 

Corresponding author: Cheng-Chi Wang


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Cite this article
Bo-lin Jian, Jui-Pin Hung, Cheng-Chi Wang, and Chun-Chang Liu, Deep Learning Model for Determining Defects of Vision Inspection Machine Using Only a Few Samples, Sens. Mater., Vol. 32, No. 12, 2020, p. 4217-4231.



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