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

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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

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 intelligent manufacturing field, as 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 in the industry. Therefore, in this study, we optimized to determine the defects of the production component by deep learning 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 analyze and establish a deep learning model to determine whether the finished products of a vision inspection machine have defects using only a few samples. We compare the pros and cons of DarkNet-53, which is a convolutional neural network that is 53 layers deep, and AlexNet, which is a deep convolutional neural network, 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 bring a novel diagnosis technique and also be helpful for the intelligent manufacturing industry.

Corresponding author: Cheng-Chi Wang

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