화장품 물체 인식을 위한 Two-Stage 딥러닝 기반 알고리즘

• 영문명: Two-Stage Deep Learning Based Algorithm for Cosmetic Object Recognition
• 발행기관: 한국산업경영시스템학회
• 저자명: 김종민 서대호
• 간행물 정보: 『산업경영시스템학회지』제46권 제4호, 101~106쪽, 전체 6쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.12.31

영문 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.

목차

1. 서론

2. 이론적 배경

3. 연구 방법

4. 실험 결과

5. 결론

키워드

Computer Vision Deep Learning Object Detection Classification