Ocean Systems Engineering

  • 제10권4호
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  • Pages.415-434
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  • 2020
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  • 2093-6702(pISSN)
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  • 2093-677X(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

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)
  • 투고 : 2020.05.14
  • 심사 : 2020.12.10
  • 발행 : 2020.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

This work is an expansion of our previous studies (Lee et al. 2018, 2019) and was partially supported by (a) Ministry of Trade, Industry and Energy, Republic of Korea, under "Development of LNG Carrier Performance and Safty Monitoring System Using Satellite Communication and 3D Visualization Technology" (20002720), and (b) Research Institute of Marine Systems Engineering of Seoul National University, Republic of Korea.

참고문헌

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피인용 문헌

  1. Survey on Deep Learning-Based Marine Object Detection vol.2021, 2020, https://doi.org/10.1155/2021/5808206
  2. Detection and tracking for the awareness of surroundings of a ship based on deep learning vol.8, pp.5, 2020, https://doi.org/10.1093/jcde/qwab053
  3. A Review of Methods for Ship Detection with Electro-Optical Images in Marine Environments vol.9, pp.12, 2020, https://doi.org/10.3390/jmse9121408