Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.