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http://dx.doi.org/10.11627/jkise.2021.44.3.001

Deep Learning based Image Recognition Models for Beef Sirloin Classification  

Han, Jun-Hee (Departement of Industrial & Management Systems Engineering, Dong-A University)
Jung, Sung-Hun (Departement of Industrial & Management Systems Engineering, Dong-A University)
Park, Kyungsu (Department of Business Administration, Pusan National University)
Yu, Tae-Sun (Division of Systems Management and Engineering, Pukyong National University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.44, no.3, 2021 , pp. 1-9 More about this Journal
Abstract
This research examines deep learning based image recognition models for beef sirloin classification. The sirloin of beef can be classified as the upper sirloin, the lower sirloin, and the ribeye, whereas during the distribution process they are often simply unified into the sirloin region. In this work, for detailed classification of beef sirloin regions we develop a model that can learn image information in a reasonable computation time using the MobileNet algorithm. In addition, to increase the accuracy of the model we introduce data augmentation methods as well, which amplifies the image data collected during the distribution process. This data augmentation enables to consider a larger size of training data set by which the accuracy of the model can be significantly improved. The data generated during the data proliferation process was tested using the MobileNet algorithm, where the test data set was obtained from the distribution processes in the real-world practice. Through the computational experiences we confirm that the accuracy of the suggested model is up to 83%. We expect that the classification model of this study can contribute to providing a more accurate and detailed information exchange between suppliers and consumers during the distribution process of beef sirloin.
Keywords
Deep learning; Image recognition; Classification; Data augmentation; Beef section;
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