An automatic recognition method for food foreign matter based on improved convolutional Neural network
Food and Machinery, ISSN: 1003-5788, Vol: 38, Issue: 7, Page: 133-137
2022
- 1Citations
- 22Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations1
- Citation Indexes1
- Usage22
- Downloads17
- Abstract Views5
Article Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85186078154&origin=inward; http://dx.doi.org/10.13652/j.spjx.1003.5788.2022.60038; https://www.ifoodmm.cn/journal/vol38/iss7/21; https://www.ifoodmm.cn/cgi/viewcontent.cgi?article=1251&context=journal; https://dx.doi.org/10.13652/j.spjx.1003.5788.2022.60038; https://www.chndoi.org/Resolution/Handler?doi=10.13652/j.spjx.1003.5788.2022.60038
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