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| Comparative Study on Image Recognition Models for Restructured Beef |
| WANG Bo, YANG Hongyao, LU Fenggui, CHEN Zidong, CAO Zhenxia, LIU Dengyong |
| 1.National and 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 Provincial Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, Nanjing 210095, China |
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Abstract Three deep residual network (ResNet) models (ResNet-50, ResNet-101 and ResNet-152) to quickly identify restructured meat were built based on machine vision technology, and they were comparatively analyzed for recognition accuracy and response time applying visual geometry group network (VGG-16) model, support vector machine (SVM) model and LeNet-5 convolution neural network model. Images of restructured beef steak samples were collected and preprocessed. As a result, a total of 6 168 images were obtained for this research, 4 936 of which were randomly selected as the training group, and the remaining 1 232 were used as the test group. The results showed that all the three ResNet models could fast and accurately identify restructured beef steak. With more convolution layers, the accuracy was higher. The ResNet-50 model exhibited higher recognition accuracy with testing time of only 0.45 s and it was a better one to accurately and quickly identify recombined ground beef.
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2020, Vol. 34 
