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Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of the method, in the study, the features of color, sha...

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Detalles Bibliográficos
Autores principales: Chen, Rui, Wang, Meiling, Lai, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340283/
https://www.ncbi.nlm.nih.gov/pubmed/32634167
http://dx.doi.org/10.1371/journal.pone.0235783
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author Chen, Rui
Wang, Meiling
Lai, Yi
author_facet Chen, Rui
Wang, Meiling
Lai, Yi
author_sort Chen, Rui
collection PubMed
description In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of the method, in the study, the features of color, shape, and texture of commodity images are first analyzed, and the basic structure of deep convolutional neural network (CNN) model is analyzed. Then, 50,000 pictures containing different commodities are constructed to verify the recognition effect of the model. Finally, the MS-CNN model is taken as the research object for improvement to explore the influence of label errors (p = 0.03, 0.05, 0.07, 0.09, 0.12) with different parameter settings and different probabilities (size of convolutional kernel, Dropout rate) on the recognition accuracy of MS-CNN model, at the same time, a CIR system platform based on MS-CNN model is built, and the recognition performance of salt and pepper noise images with different SNR (0, 0.03, 0.05, 0.07, 0.1) was compared, then the performance of the algorithm in the actual image recognition test was compared. The results show that the recognition accuracy is the highest (97.8%) when the convolution kernel size in the MS-CNN model is 2*2 and 3*3, and the average recognition accuracy is the highest (97.8%) when the dropout rate is 0.1; when the error probability of picture label is 12%, the recognition accuracy of the model constructed in this study is above 96%. Finally, the commodity image database constructed in this study is used to identify and verify the model. The recognition accuracy of the algorithm in this study is significantly higher than that of the Minitch stochastic gradient descent algorithm under different SNR conditions, and the recognition accuracy is the highest when SNR = 0 (99.3%). The test results show that the model proposed in this study has good recognition effect in the identification of commodity images in scenes of local occlusion, different perspectives, different backgrounds, and different light intensity, and the recognition accuracy is 97.1%. To sum up, the CIR platform based on MS-CNN model constructed in this study has high recognition accuracy and robustness, which can lay a foundation for the realization of subsequent intelligent commodity recognition technology.
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spelling pubmed-73402832020-07-16 Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network Chen, Rui Wang, Meiling Lai, Yi PLoS One Research Article In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of the method, in the study, the features of color, shape, and texture of commodity images are first analyzed, and the basic structure of deep convolutional neural network (CNN) model is analyzed. Then, 50,000 pictures containing different commodities are constructed to verify the recognition effect of the model. Finally, the MS-CNN model is taken as the research object for improvement to explore the influence of label errors (p = 0.03, 0.05, 0.07, 0.09, 0.12) with different parameter settings and different probabilities (size of convolutional kernel, Dropout rate) on the recognition accuracy of MS-CNN model, at the same time, a CIR system platform based on MS-CNN model is built, and the recognition performance of salt and pepper noise images with different SNR (0, 0.03, 0.05, 0.07, 0.1) was compared, then the performance of the algorithm in the actual image recognition test was compared. The results show that the recognition accuracy is the highest (97.8%) when the convolution kernel size in the MS-CNN model is 2*2 and 3*3, and the average recognition accuracy is the highest (97.8%) when the dropout rate is 0.1; when the error probability of picture label is 12%, the recognition accuracy of the model constructed in this study is above 96%. Finally, the commodity image database constructed in this study is used to identify and verify the model. The recognition accuracy of the algorithm in this study is significantly higher than that of the Minitch stochastic gradient descent algorithm under different SNR conditions, and the recognition accuracy is the highest when SNR = 0 (99.3%). The test results show that the model proposed in this study has good recognition effect in the identification of commodity images in scenes of local occlusion, different perspectives, different backgrounds, and different light intensity, and the recognition accuracy is 97.1%. To sum up, the CIR platform based on MS-CNN model constructed in this study has high recognition accuracy and robustness, which can lay a foundation for the realization of subsequent intelligent commodity recognition technology. Public Library of Science 2020-07-07 /pmc/articles/PMC7340283/ /pubmed/32634167 http://dx.doi.org/10.1371/journal.pone.0235783 Text en © 2020 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chen, Rui
Wang, Meiling
Lai, Yi
Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network
title Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network
title_full Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network
title_fullStr Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network
title_full_unstemmed Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network
title_short Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network
title_sort analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340283/
https://www.ncbi.nlm.nih.gov/pubmed/32634167
http://dx.doi.org/10.1371/journal.pone.0235783
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