Earticle

초록

영어

Ship collision accidents not only cause loss of life and property damage, but also cause marine pollution and can become national disasters, so prevention is very important. Most of these ship collision accidents are caused by human factors due to the navigation officer's lack of vigilance and carelessness, and in many cases, they can be prevented through the support of a system that helps with situation awareness. Recently, artificial intelligence has been used to develop systems that help navigators recognize the situation, but the sea is very wide and deep, so it is difficult to secure maritime traffic datasets, which also makes it difficult to develop artificial intelligence models. In this paper, to solve these difficulties, we propose a method to build a dataset with characteristics similar to actual maritime traffic datasets. The proposed method uses segmentation and inpainting technologies to build a foreground and background dataset, and then applies compositing technology to create a synthetic dataset. Through prototype implementation and result analysis of the proposed method, it was confirmed that the proposed method is effective in overcoming the difficulties of dataset construction and complementing various scenes similar to reality.

목차

Abstract
1. 서론
2. 연구 배경
2.1 인공지능 학습용 데이터
2.2 합성 데이터
2.3 합성 데이터 생성 기술
3. 합성 데이터셋 생성 방안
3.1 전경 데이터셋 생성
3.2 배경 데이터셋 생성
3.3 합성 장면 생성
3.4 프로토타입 구축
3.5 한계점
4. 결론
References

키워드

저자

• 이영채 [ Youngchae Lee | Graduate School of Environmental Engineering, Chungnam National University ] First Author
• 박세길 [ Sekil Park | Principal Researcher, KRISO (Korea Research Institute of Ships & Ocean Engineering) ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005