<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>High-speed assembly line food packaging quality defect detection methods have poor error detection rates, missing rates and accuracy. This paper advances a process of computer-aided online monitoring of food packaging based on a deep neural network model. Firstly, this paper uses the deep convolution method to analyze the defects in food packaging. Then the convolution method of food packaging defects is improved. The correct identification of defects in food packaging can be enhanced by adjusting VGG16. This paper uses a convolutional neural network, transfer learning and adaptive neural network to compare the recognition effect of food packaging defects based on a forward neural network. It is proved that the recognition accuracy of this method is 0.0005. Good identification results can be obtained after 10 times of repeated practices. This method has a good classification effect.</p>
</abstract>

High-speed assembly line food packaging quality defect detection methods have poor error detection rates, missing rates and accuracy. This paper advances a process of computer-aided online monitoring of food packaging based on a deep neural network model. Firstly, this paper uses the deep convolution method to analyze the defects in food packaging. Then the convolution method of food packaging defects is improved. The correct identification of defects in food packaging can be enhanced by adjusting VGG16. This paper uses a convolutional neural network, transfer learning and adaptive neural network to compare the recognition effect of food packaging defects based on a forward neural network. It is proved that the recognition accuracy of this method is 0.0005. Good identification results can be obtained after 10 times of repeated practices. This method has a good classification effect.

Computer-Aided Design of Hand-Drawn art Food Packaging Design Based on Deep Neural Network Model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

High-speed assembly line food packaging quality defect detection methods have poor error detection rates, missing rates and accuracy. This paper advances a...

Computer-Aided Design of Hand-Drawn art Food Packaging Design Based on Deep Neural Network Model

Data publikacji: 06 cze 2023

Zakres stron: 2043 - 2052

Otrzymano: 26 cze 2022

Przyjęty: 25 lis 2022

DOI: https://doi.org/10.2478/amns.2023.1.00308

Słowa kluczowe
Neural network model, Food packaging, Defect detection, Image evaluation, Computer-aided design

© 2023 Xiaobing Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Computer-Aided Design of Hand-Drawn art Food Packaging Design Based on Deep Neural Network Model

Data publikacji: 06 cze 2023

Zakres stron: 2043 - 2052

Otrzymano: 26 cze 2022

Przyjęty: 25 lis 2022

DOI: https://doi.org/10.2478/amns.2023.1.00308

Słowa kluczoweNeural network model, Food packaging, Defect detection, Image evaluation, Computer-aided design

© 2023 Xiaobing Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Neural network model, Food packaging, Defect detection, Image evaluation, Computer-aided design