Skip to content
Extra Form
Abstract Manually converting the information of objects existing in the ship safety plan into digital data requires significant effort and time. To overcome this problem, a technique is required for automatically extracting the location and information of the object in the plan. However, owing to the characteristics of the ship safety plan, there are frequent cases in which the detection target overlaps with noise (figure, text, etc.), which lowers
the detection accuracy. In this study, an object detection model based on a convolutional neural network is proposed to extract the number and location of objects effectively in a ship safety plan while incorporating noise. Among various deep learning models, suitable models for object detection in ship safety plans were compared and analyzed. In addition, an algorithm to generate the data necessary for training the object detection model was proposed. Subsequently, a specialized detection algorithm to rapidly process a large ship safety plan was proposed. The method proposed in this study was applied to 15 ship safety plans. Consequently, an average recall of 0.85 was achieved, confirming the effectiveness of the proposed method.
Publication Date 2022-02-15
Role Corresponding Author
Category SCI
Impact Factor 3.795

Min-Chul Kong, Myung-Il Roh, Ki-Su Kim, Jeongyoul Lee, Jongoh Kim, Gapheon Lee, "Object Detection Method for Ship Safety Plans Using Deep Learning", Ocean Engineering, Vol. 246, pp. 110587.1-15, 2022.02.15

https://doi.org/10.1016/j.oceaneng.2022.110587


  1. No Image 06Jun
    by SyDLab
    in International Journal

    Hye-Won Lee, Myung-Il Roh, Ki-Su Kim, “Ship Route Planning in Arctic Ocean Based on POLARIS”, Ocean Engineering, Vol. 234, pp. 109297.1-14, 2021.08.15

  2. No Image 09Dec
    by SyDLab
    in International Journal

    Hye-Won Lee, Joo-Hyun Woo, Myung-Il Roh, Seung-Ho Ham, et al., "Integrated Simulation of Virtual Prototypes and Control Algorithms of Unmanned Surface Vehicles Based on a Robot Operating System", Journal of Marine Science and Technology (Taiwan)

  3. No Image 12Aug
    by SyDLab
    in International Journal

    Won-Jae Lee, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Yeong-Min Cho, Nam-Sun Son, “Detection and Tracking for the Awareness of Surroundings of a Ship Based on Deep Learning”, Journal of Computational Design and Engineering, Vo. 8, No. 5

  4. No Image 10Dec
    by SyDLab
    in International Journal

    Ki-Su Kim, Myung-Il Roh, Seung-Min Lee, "Quasi-static Flooding Analysis Method of a Damaged Ship Considering Oil Spill and Cargo Load", Journal of Ship Production and Design, 2022.02.10

  5. No Image 07Jan
    by SyDLab
    in International Journal

    Min-Chul Kong, Myung-Il Roh, Ki-Su Kim, Jeongyoul Lee, Jongoh Kim, Gapheon Lee, "Object Detection Method for Ship Safety Plans Using Deep Learning", Ocean Engineering, 2022.02.15

  6. No Image 11Jul
    by SyDLab
    in International Journal

    Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, "Automation of Crane Control for Block Lifting Based on Deep Reinforcement Learning", Journal of Computational Design and Engineering, Vol. 9, No. 4, pp. 1430- 1448, 2022.07.23

  7. No Image 21Oct
    by SyDLab
    in International Journal

    Jin-Hyeok Kim, Myung-Il Roh et al., "Prediction of the Superiority of the Hydrodynamic Performance of Hull Forms Using Deep Learning", International Journal of Naval Architecture and Ocean Engineering, Vol. 49, pp. 100490.1-15, pp. 2022.11.01

  8. No Image 15Nov
    by SyDLab
    in International Journal

    Min-Chul Kong, Myung-Il Roh, Ki-Su Kim, Ho-Gyun Park, Jong-Oh Kim, "Variable Indexing Method in Rule Documents for Ship Design Using Extraction of PDF Elements", Journal of Computational Design and Engineering, Vol. 9, No. 6, 2022.11.16

Board Pagination Prev 1 2 3 4 5 6 Next
/ 6

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