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.

1. 서 론
2. 관련 문헌 연구
3. 이미지 어노테이션 자동화 프로세스
    3.1 군집 분석 기반 주석 할당
    3.2 SHAP 기반 위치 정보 추출
4. 분석 및 결과
    4.1 데이터 소개
    4.2 군집 기반의 라벨 도출
    4.3 제안 프로세스 적용
5. 결 론
Acknowledgement
References

• Jin Hyeong Jung(Department of Industrial and Systems Engineering, Kyonggi University Graduate School) | 정진형 (경기대학교 일반대학원 산업시스템공학과)
• Hyun Su Sim(Department of Industrial and Systems Engineering, Kyonggi University Graduate School) | 심현수 (경기대학교 일반대학원 산업시스템공학과)
• Yong Soo Kim(Department of Industrial and Systems Engineering, Kyonggi University) | 김용수 (경기대학교 산업시스템공학과) Corresponding Author