SHAP를 이용한 이미지 어노테이션 자동화 프로세스 연구

• 영문명: A Study on Image Annotation Automation Process using SHAP for Defect Detection
• 발행기관: 한국산업경영시스템학회
• 저자명: 정진형 심현수 김용수
• 간행물 정보: 『산업경영시스템학회지』제46권 제1호, 76~83쪽, 전체 8쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.03.31

영문 초록

Recently, the development of computer vision with deep learning has made object detection using images applicable to diverse fields, such as medical care, manufacturing, and transportation. The manufacturing industry is saving time and money by applying computer vision technology to detect defects or issues that may occur during the manufacturing and inspection process. Annotations of collected images and their location information are required for computer vision technology. However, manually labeling large amounts of images is time-consuming, expensive, and can vary among workers, which may affect annotation quality and cause inaccurate performance. This paper proposes a process that can automatically collect annotations and location information for images using eXplainable AI, without manual annotation. If applied to the manufacturing industry, this process is thought to save the time and cost required for image annotation collection and collect relatively high-quality annotation information.

목차

1. 서론

2. 관련 문헌 연구

3. 이미지 어노테이션 자동화 프로세스

4. 분석 및 결과

5. 결론

키워드

Image Annotation SHAP Explainable Artificial Intelligence(XAI) Automation Process Defect Detection