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食品与发酵工业  2021, Vol. 47 Issue (1): 259-265    DOI: 10.13995/j.cnki.11-1802/ts.024419
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糖熏鸡腿颜色快速精准识别的多层卷积神经网络模型研究
王博1, 杨洪遥1, 陆逢贵1, 陈子东2, 曹振霞1, 刘登勇1,3*
1(渤海大学 食品科学与工程学院,生鲜农产品贮藏加工及安全控制技术国家地方联合工程研究中心,辽宁 锦州,121013)
2(哈尔滨商业大学 职业技术教育学院,黑龙江 哈尔滨,150028)
3(江苏省肉类生产与加工质量安全控制协同创新中心,江苏 南京,210095)
Vision research on CNN model for quick and accurate identification of sugar-smoked chicken thighs color
WANG Bo1, YANG Hongyao1, LU Fenggui1, CHEN Zidong2, CAO Zhenxia1, LIU Dengyong1,3*
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)
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摘要 为快速精准识别糖熏鸡腿在熏制过程中产生的所有颜色,基于机器视觉技术,构建Xception-CNN模型用于熏鸡腿颜色的识别,同时应用ResNet-50、Inception和传统卷积神经网络(convolutional neural networks,CNN)等3种模型对比分析Xception-CNN模型对熏鸡腿颜色的识别效果。采集并经过图像预处理后,共得到不同颜色的熏鸡腿图像4 352张,作为4种模型的实验样本,随机选取其中的3 482张作为训练组,剩下的870张作为测试组。结果表明,4种模型的平均识别准确率分别为92%(Xception-CNN)、91%(ResNet-50)、89%(Inception)、87%(传统CNN);测试时间分别为1.36 s(Xception-CNN)、0.81 s(ResNet-50)、0.98 s(Inception)、2.48 s(传统CNN)。Xception-CNN模型对糖熏鸡腿图像的颜色识别准确率最高,达到92%,测试时间略高于ResNet-50模型和Inception模型,但低于传统CNN模型,仅需1.36 s即可完成识别,此模型可以实现糖熏鸡腿颜色的快速精准识别,为糖熏工艺参数精准调控、保障产品颜色标准化等提供可靠依据。
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王博
杨洪遥
陆逢贵
陈子东
曹振霞
刘登勇
关键词:  熏鸡  糖熏  颜色识别  机器视觉  多层卷积神经网络模型    
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
收稿日期:  2020-05-10      修回日期:  2020-08-10                发布日期:  2021-02-03      期的出版日期:  2021-01-15
基金资助: 国家重点研发计划项目(2016YFD0401505);辽宁省“兴辽英才计划”项目(XLYC1 807 100);辽宁省一流学科项目(LNSPXKBD2020204,LNSPXKBD2020306)
作者简介:  王博讲师和杨洪遥硕士研究生为共同第一作者(刘登勇教授为通讯作者,E-mail: jz_dyliu@126.com)
引用本文:    
王博,杨洪遥,陆逢贵,等. 糖熏鸡腿颜色快速精准识别的多层卷积神经网络模型研究[J]. 食品与发酵工业, 2021, 47(1): 259-265.
WANG Bo,YANG Hongyao,LU Fenggui,et al. 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.
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