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.

1. 서 론
2. 배경 지식 및 선행 연구
3. 문제정의 및 방법론
    3.1 데이터 증강
    3.2 Teachable Machine과 MobileNet을 활용한이미지 인식 기법
4. 실험결과
    4.1 데이터 증강 기법 실험
    4.2 학습 모델 실험 결과
5. 결 론
References

• Jun-Hee Han(동아대학교 산업경영공학과) | 한준희
• Sung-Hun Jung(부산대학교 경영학과) | 정성훈
• Kyungsu Park(부산대학교 경영학과) | 박경수
• Tae-Sun Yu(부경대학교 시스템경영공학부) | 유태선 Corresponding Author