Two-Stage Deep Learning Based Algorithm for Cosmetic Object Recognition

서지정보

ㆍ발행기관 : 한국산업경영시스템학회 ㆍ수록지정보 : 산업경영시스템학회지 / 46권 / 4호
ㆍ저자명 : 김종민, 서대호

목차

1. 서 론
2. 이론적 배경
2.1 YOLO(You Only Look Once)
2.2 ResNet
2.3 MobileNet
2.4 EfficientNet
2.5 전이 학습(Transfer Learning)
3. 연구 방법
3.1 YOLO을 활용한 화장품 후보군 검출
3.2 데이터 문제 및 방안 제시
3.3 EfficientNet의 구조 변경 및 학습 방법론
4. 실험 결과
4.1 실험 데이터 구축
4.2 물체 검출 모델의 성능 평가
4.3 분류 모델 비교 실험
4.4 정성적 결과
5. 결 론
References

영어 초록

With the recent surge in YouTube usage, there has been a proliferation of user-generated videos where individuals evaluate cosmetics. Consequently, many companies are increasingly utilizing evaluation videos for their product marketing and market research. However, a notable drawback is the manual classification of these product review videos incurring significant costs and time. Therefore, this paper proposes a deep learning-based cosmetics search algorithm to automate this task. The algorithm consists of two networks: One for detecting candidates in images using shape features such as circles, rectangles, etc and Another for filtering and categorizing these candidates. The reason for choosing a Two-Stage architecture over One-Stage is that, in videos containing background scenes, it is more robust to first detect cosmetic candidates before classifying them as specific objects. Although Two-Stage structures are generally known to outperform One-Stage structures in terms of model architecture, this study opts for Two-Stage to address issues related to the acquisition of training and validation data that arise when using One-Stage. Acquiring data for the algorithm that detects cosmetic candidates based on shape and the algorithm that classifies candidates into specific objects is cost-effective, ensuring the overall robustness of the algorithm.

참고 자료

없음