Assessment of ship-block stacking status based on images obtained from drone and geographic information system data

Author:

Han Young-SooORCID,Lee KyunghoORCID,Nam ByeongwookORCID,Kim Yang Ouk,Kim Youngsu,Yeo Hyeon-Bin

Funder

Inha University

Ministry of Trade, Industry and Energy

Korea Evaluation Institute of Industrial Technology

Publisher

Elsevier BV

Reference23 articles.

1. Deep learning based efficient ship detection from drone-captured images for maritime surveillance;Cheng;Ocean Eng.,2023

2. A spatial scheduling system for block painting process in shipbuilding;Cho;CIRP Annals,2001

3. Detection of ships moving in a straight line and turning with implications for wide area surveillance;Jaiswal;Int. J. Nav. Archit. Ocean Eng.,2022

4. Transporter scheduling for block transportation in the shipyard;Joo,2005

5. Block Transportation Management System;Jung,2007

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1. Corrosion Area Detection and Depth Prediction using Machine Learning;International Journal of Naval Architecture and Ocean Engineering;2024-09

2. Multi-vessel target tracking with camera fusion for unmanned surface vehicles;International Journal of Naval Architecture and Ocean Engineering;2024

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