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Ocean Systems Engineering
  Volume 10, Number 4, December 2020 , pages 415-434
DOI: https://doi.org/10.12989/ose.2020.10.4.415
 

Image-based ship detection using deep learning
Sung-Jun Lee, Myung-Il Roh and Min-Jae Oh

 
Abstract
    Detecting objects is important for the safe operation of ships, and enables collision avoidance, risk detection, and autonomous sailing. This study proposes a ship detection method from images and videos taken at sea using one of the state-of-the-art deep neural network-based object detection algorithms. A deep learning model is trained using a public maritime dataset, and results show it can detect all types of floating objects and classify them into ten specific classes that include a ship, speedboat, and buoy. The proposed deep learning model is compared to a universal trained model that detects and classifies objects into general classes, such as a person, dog, car, and boat, and results show that the proposed model outperforms the other in the detection of maritime objects. Different deep neural network structures are then compared to obtain the best detection performance. The proposed model also shows a real-time detection speed of approximately 30 frames per second. Hence, it is expected that the proposed model can be used to detect maritime objects and reduce risks while at sea.
 
Key Words
    object detection; ship detection; deep neural network; deep learning; maritime dataset
 
Address
Sung-Jun Lee: Department of Naval Architecture and Ocean Engineering, Seoul National University, Republic of Korea
Myung-Il Roh: Department of Naval Architecture and Ocean Engineering, and Research Institute of Marine Systems Engineering, Seoul National University, Seoul, Republic of Korea
Min-Jae Oh: School of Naval Architecture and Ocean Engineering, University of Ulsan, Ulsan, Republic of Korea
 

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