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http://dx.doi.org/10.12989/ose.2020.10.4.415

Image-based ship detection using deep learning  

Lee, Sung-Jun (Department of Naval Architecture and Ocean Engineering, Seoul National University)
Roh, Myung-Il (Department of Naval Architecture and Ocean Engineering, and Research Institute of Marine Systems Engineering, Seoul National University)
Oh, Min-Jae (School of Naval Architecture and Ocean Engineering, University of Ulsan)
Publication Information
Ocean Systems Engineering / v.10, no.4, 2020 , pp. 415-434 More about this Journal
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.
Keywords
object detection; ship detection; deep neural network; deep learning; maritime dataset;
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