Abstract
Objective: Improve the speed and accuracy of foreign matter identification in food. Methods: Based on the LeNet-5 network structure, the improved CNN model was obtained by adding batch normalization layer and dropout layer. Using this model, a recognition system was established for the automatic recognition of foreign bodies in food images. The performance of the model was analyzed through experiments. Results: Compared with the traditional model, this model has higher detection accuracy and faster recognition speed. The recognition accuracy of food foreign bodies was 99.75% and the recognition time was only 0.332 s. Conclusion: The foreign object recognition model of dumpling image had good detection speed and recognition accuracy.
Publication Date
10-16-2022
First Page
133
Last Page
137
DOI
10.13652/j.spjx.1003.5788.2022.60038
Recommended Citation
A-qin, DENG and Ping-xia, Hu
(2022)
"An automatic recognition method for food foreign matter based on improved convolutional Neural network,"
Food and Machinery: Vol. 38:
Iss.
7, Article 21.
DOI: 10.13652/j.spjx.1003.5788.2022.60038
Available at:
https://www.ifoodmm.cn/journal/vol38/iss7/21
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