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Field Applicability Study of Hull Crack Detection Based on Artificial Intelligence

인공지능 기반 선체 균열 탐지 현장 적용성 연구

  • 송상호 ((사)한국선급 디지털라이제이션팀) ;
  • 이갑헌 ((사)한국선급 디지털라이제이션팀) ;
  • 한기민 ((사)한국선급 디지털라이제이션팀) ;
  • 장화섭 ((사)한국선급 디지털라이제이션팀)
  • Received : 2022.04.22
  • Accepted : 2022.05.18
  • Published : 2022.08.20

Abstract

With the advent of autonomous ships, it is emerging as one of the very important issues not only to operate with a minimum crew or unmanned ships, but also to secure the safety of ships to prevent marine accidents. On-site inspection of the hull is mainly performed by the inspector's visual inspection, and video information is recorded using a small camera if necessary. However, due to the shortage of inspection personnel, time and space constraints, and the pandemic situation, the necessity of introducing an automated inspection system using artificial intelligence and remote inspection is becoming more important. Furthermore, research on hardware and software that enables the automated inspection system to operate normally even under the harsh environmental conditions of a ship is absolutely necessary. For automated inspection systems, it is important to review artificial intelligence technologies and equipment that can perform a variety of hull failure detection and classification. To address this, it is important to classify the hull failure. Based on various guidelines and expert opinions, we divided them into 6 types(Crack, Corrosion, Pitting, Deformation, Indent, Others). It was decided to apply object detection technology to cracks of hull failure. After that, YOLOv5 was decided as an artificial intelligence model suitable for survey and a common hull crack dataset was trained. Based on the performance results, it aims to present the possibility of applying artificial intelligence in the field by determining and testing the equipment required for survey.

Keywords

Acknowledgement

이 논문은 2022년도 해양수산부 및 해양수산과학기술진흥원 연구비 지원으로 수행된 '자율운항선박 기술개발사업(20200615)'의 연구결과입니다.