딥러닝 이미지 인식 기술을 활용한 소고기 등심 세부 부위 분류

• 영문명: Deep Learning based Image Recognition Models for Beef Sirloin Classification
• 발행기관: 한국산업경영시스템학회
• 저자명: 한준희(Jun-Hee Han) 정성훈(Sung-Hun Jung) 박경수(Kyungsu Park) 유태선(Tae-Sun Yu)
• 간행물 정보: 『산업경영시스템학회지』제44권 제3호, 1~9쪽, 전체 9쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2021.09.30

영문 초록

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.

목차

1. 서론

2. 배경 지식 및 선행 연구

3. 문제정의 및 방법론

4. 실험결과

5. 결론

키워드

Deep learning Image recognition Classification Data augmentation Beef section