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.

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

• 김종민(다겸 주식회사) | Jongmin Kim (Dagyeom, CO. LTD)
• 서대호(다겸 주식회사) | Daeho Seo (Dagyeom, CO. LTD) Corresponding author