한국해양공학회지 (Journal of Ocean Engineering and Technology)

  • 제35권1호
  • /
  • Pages.91-97
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  • 2021
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  • 1225-0767(pISSN)
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  • 2287-6715(eISSN)
한국해양공학회 (Korean Society of Ocean Engineers)
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Fundamental Research for Video-Integrated Collision Prediction and Fall Detection System to Support Navigation Safety of Vessels

  • Kim, Bae-Sung (Ocean ICT & Advanced Materials Technology Research Division, RIMS) ;
  • Woo, Yun-Tae (Ocean ICT & Advanced Materials Technology Research Division, RIMS) ;
  • Yu, Yung-Ho (Research Institute of Ocean and Ship ICT Convergence, MEIPA) ;
  • Hwang, Hun-Gyu (Ocean ICT & Advanced Materials Technology Research Division, RIMS)
  • 투고 : 2020.11.27
  • 심사 : 2021.01.21
  • 발행 : 2021.02.28
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초록

Marine accidents caused by ships have brought about economic and social losses as well as human casualties. Most of these accidents are caused by small and medium-sized ships and are due to their poor conditions and insufficient equipment compared with larger vessels. Measures are quickly needed to improve the conditions. This paper discusses a video-integrated collision prediction and fall detection system to support the safe navigation of small- and medium-sized ships. The system predicts the collision of ships and detects falls by crew members using the CCTV, displays the analyzed integrated information using automatic identification system (AIS) messages, and provides alerts for the risks identified. The design consists of an object recognition algorithm, interface module, integrated display module, collision prediction and fall detection module, and an alarm management module. For the basic research, we implemented a deep learning algorithm to recognize the ship and crew from images, and an interface module to manage messages from AIS. To verify the implemented algorithm, we conducted tests using 120 images. Object recognition performance is calculated as mAP by comparing the pre-defined object with the object recognized through the algorithms. As results, the object recognition performance of the ship and the crew were approximately 50.44 mAP and 46.76 mAP each. The interface module showed that messages from the installed AIS were accurately converted according to the international standard. Therefore, we implemented an object recognition algorithm and interface module in the designed collision prediction and fall detection system and validated their usability with testing.

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참고문헌

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