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Abstract This study uses deep learning to introduce an automated approach for inspecting a ship’s safety plans. We compared Convolutional Neural Network (CNN) models and selected the most suitable for symbol detection. Due to the challenge of obtaining sufficient training data, we proposed a method for generating virtual data based on existing safety plans, addressing overfitting concerns. A prototype program was developed and tested on sample safety plans, showcasing its effectiveness in reviewing safety plans by accurately identifying various symbols.
Publication Date 2024-06-17

In-Chang Yeo, Myung-Il Roh, Min-Chul Kong, Dongki Min, Dongguen Jeong, "An Automated Method for the Review of a Ship’s Safety Plan Based on Deep Learning", Proceedings of ISOPE 2024, Rhodos, Greece, 2024.06.16-21


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번호 분류 제목 Publication Date
» International Conference In-Chang Yeo, Myung-Il Roh, Min-Chul Kong, Dongki Min, Dongguen Jeong, "An Automated Method for the Review of a Ship’s Safety Plan Based on Deep Learning", Proceedings of ISOPE 2024, Rhodos, Greece, 2024.06.16-21 file 2024-06-17
4 International Conference Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo, "A Method for the Automatic Generation of Hull Form Surfaces Based on MLP (Multi-Layer Perceptron) Considering Design Requirements", Proceedings of G-NAOE 2024, Southampton, UK, 2024.11.05-09 2024-11-05
3 International Conference In-Su Han, Myung-Il Roh, Min-Chul Kong, Jeongyoul Lee, Seoyoon Park, "A Search Method for Ship Regulations Considering Document Features", Proceedings of ICCAS 2024, Genoa, Italy, 2024.09.10-12 2024-09-10
2 International Conference Ha-Yun Kim, Myung-Il Roh, Jisang Ha, In-Chang Yeo, Nam-Sun Son, "A Learning-based Method for Tracking Maritime Obstacles in Real-time", Proceedings of ISOPE 2024, Rhodos, Greece, 2024.06.16-21 file 2024-06-17
1 International Conference Min-Chul Kong, Myung-Il Roh, In-Su Han, Mijin Kim, Jeoungyoun Kim, "A Method for Pipe Auto-routing Using Graph and Octree Structure", Proceedings of G-NAOE 2024, Southampton, UK, 2024.11.05-09 2024-11-05
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