Skip to content
Extra Form
Abstract Autonomous ships require an awareness system to recognize surrounding obstacles during navigation. In general, the awareness system uses data obtained by sensors such as RADAR (RAdio Detection And Ranging) and AIS (Automatic Identification System). However, autonomous ships operating in the marine environment should recognize small obstacles, including buoys that cannot be detected by RADAR and boats without the AIS system. Just as the tracking accuracy is improved by the fusion of sensors with different characteristics, a camera with a wide-angle of view or a camera capable of night detection can complement each other. In addition, tracking accuracy can be further improved if camera images from multiple viewpoints can be used in the case of detection through multiple ships. Therefore, in this study, camera-based detection and tracking methods were developed to be aware of marine obstacles that could not be recognized by RADAR or AIS. Firstly, the Convolutional Neural Network (CNN) based YOLO v5 model, combined with an attention module, was suggested for obstacle detection. Subsequently, the position and speed of the detected obstacles were tracked using the extended Kalman Filter. Finally, the tracks of the obstacles from various cameras mounted on multiple ships were integrated using the sensor fusion method to increase the accuracy. The field tests were conducted with a USV (Unmanned Surface Vessel) for various ship encountering scenarios to verify the developed methods in the actual marine environment. As a result, it was confirmed that stable and accurate detection and tracking of marine obstacles was performed using multiple cameras.
Publication Date 2022-04-28

Jeong-Ho Park, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Yeong-Min Jo, Nam-Sun Son, "Detection and Tracking Methods of Maritime Obstacles Using Multiple Cameras", Proceedings of ICDM(International Conference on Decarbonization and Digitalization in Marine Engineering) 2022, Siheung, Korea, 2022.04.28-29


  1. 김진혁, 노명일, 여인창, "선형 설계를 위한 GNN의 적용 방안 연구", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 450, 2023.11.02-03

    CategoryDomestic Conference
    Read More
  2. 하지상, 노명일, 공민철, 김미진, 김정연, "선박 유닛 모듈의 배관 배치 방법", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 114, 2023.11.02-03

    CategoryDomestic Conference
    Read More
  3. 여인창, 노명일, 공민철, 유동훈, 진은석, "LIDAR를 이용한 선박의 자동 접이안 경로 생성 알고리즘", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 181, 2023.11.02-03

    CategoryDomestic Conference
    Read More
  4. Yeong-min Jo, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Do-Hyun Chun, Min-chul Kong, "An Improved Method for the Sensor Fusion for Autonomous Ships", Proceedings of 10th PAAMES and AMEC 2023, Kyoto, Japan, 2023.10.18-20

    CategoryInternational Conference
    Read More
  5. 공민철, 노명일, 여인창, 민동기, 정동근, "그래프를 활용한 P&ID 내 장비의 연결 관계 표현 및 분석", 2023년도 한국CDE학회 하계학술발표회, 제주, p. 53, 2023.08.23-26

    CategoryDomestic Conference
    Read More
  6. 공민철, 노명일, 하지상, 김미진, 김정연, "GNN 기반 P&ID의 패턴 인식 및 분석 방법", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 108, 2023.11.02-03

    CategoryDomestic Conference
    Read More
  7. Min-Chul Kong, Myung-Il Roh, Jisang Ha, Mijin Kim, Jeoungyoun Kim, "A Method for the Generation of Optimal Patterns for Equipment Unit Modules in the Engine Room", Proceedings of 10th PAAMES and AMEC 2023, Kyoto, Japan, 2023.10.18-20

    CategoryInternational Conference
    Read More
  8. 김하연, 노명일, 하지상, 조영민, 이혜원, "센서 데이터를 활용한 딥 러닝 기반 해상 장애물의 추적 방법", 2023년도 한국CDE학회 하계학술발표회, 제주, pp. 12, 2023.08.23-26

    CategoryDomestic Conference
    Read More
  9. 한인수, 노명일, 공민철, "딥 러닝을 활용한 P&ID 내 장비 인식 방법", 2023년도 한국CDE학회 하계학술발표회, 제주, pp. 11, 2023.08.23-26

    CategoryDomestic Conference
    Read More
  10. 여인창, 노명일, 공민철, 민동기, 정동근, "선박의 Safety Plan 검토를 위한 자동 데이터 생성과 딥 러닝 기반 객체 검출", 2023년도 한국CDE학회 하계학술발표회, 제주, pp. 13, 2023.08.23-26

    CategoryDomestic Conference
    Read More
Board Pagination Prev 1 2 3 4 5 6 7 8 9 10 ... 51 Next
/ 51

Powered by Xpress Engine / Designed by Sketchbook

sketchbook5, 스케치북5

sketchbook5, 스케치북5

나눔글꼴 설치 안내


이 PC에는 나눔글꼴이 설치되어 있지 않습니다.

이 사이트를 나눔글꼴로 보기 위해서는
나눔글꼴을 설치해야 합니다.

설치 취소