Food and Fermentation Industries >

2021 , Vol. 47 >Issue 1: 259 - 265

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.024419

Vision research on CNN model for quick and accurate identification of sugar-smoked chicken thighs color

  • WANG Bo ,
  • YANG Hongyao ,
  • LU Fenggui ,
  • CHEN Zidong ,
  • CAO Zhenxia ,
  • LIU Dengyong
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  • 1(National & Local Joint Engineering Research Center of Storage, Processing and Safety Control Technology for Fresh Agricultural and Aquatic Products, College of Food Science and Technology, Bohai University, Jinzhou 121013, China)
    2(School of Vocational and Technical Education, Harbin University of Commerce, Harbin 150028, China)
    3(Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, Nanjing 210095, China)

Received date: 2020-05-10

  Revised date: 2020-08-10

  Online published: 2021-02-03

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Abstract

To explore the machine vision model that can quickly and accurately identify all the colors of smoked chicken legs during the smoking process, this article builds an Xception-CNN model for the recognition of smoked chicken leg colors based on machine vision technology, while compares and analyzes the Xception-CNN model’s recognition effect on smoked chicken leg color by applying ResNet-50, Inception and traditional CNN and other three models.A total of 4 352 smoked chicken leg images of different colors were collected and image pre-processed as experimental samples of four models, of which 3 482 were randomly selected as the training group, and the remaining 870 as the test group.The average recognition accuracy of the four models is Xception-CNN-92%, ResNet-50-91%, Inception-89%, traditional CNN-87%, and test time is Xception-CNN-1.36 s, ResNet-50-0.81 s, Inception-0.98 s, traditional CNN-2.48 s.Therefore, the Xception-CNN model has the highest color recognition accuracy for sugar-smoked chicken leg images, reaching 92%.The test time is slightly higher than the ResNet-50 model and the Inception model, but lower than the traditional CNN model.It only takes 1.36 s to complete the recognition.The fast and accurate identification of the color of smoked chicken legs can provide a reliable basis for the precise control of the parameters of the sugar smoked process and the standardization of product colors.

Key words： smoked chicken; sugar smoked; color recognition; machine vision; multilayer convolutional neural network model

Cite this article

WANG Bo , YANG Hongyao , LU Fenggui , CHEN Zidong , CAO Zhenxia , LIU Dengyong . Vision research on CNN model for quick and accurate identification of sugar-smoked chicken thighs color[J]. Food and Fermentation Industries, 2021 , 47(1) : 259 -265 . DOI: 10.13995/j.cnki.11-1802/ts.024419

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