Abstract
Deep-sea organism automatic tracking has rarely been studied because of a lack of training data. However, it is extremely important for underwater robots to recognize and to predict the behavior of organisms. In this paper, we first develop a method for underwater real-time recognition and tracking of multi-objects, which we call “You Only Look Once: YOLO”. This method provides us with a very fast and accurate tracker. At first, we remove the haze, which is caused by the turbidity of the water from a captured image. After that, we apply YOLO to allow recognition and tracking of marine organisms, which include shrimp, squid, crab and shark. The experiments demonstrate that our developed system shows satisfactory performance.
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07 July 2020
The original version of this article unfortunately contained a mistake in the Affiliation section.
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Acknowledgements
This work was supported by Leading Initiative for Excellent Young Researcher (LEADER) of MEXT-Japan (16809746), Grants-in-Aid for Scientific Research of JSPS (17K14694), Research Fund of State Key Laboratory of Marine Geology at Tongji University (MGK1608),, Research Fund of The Telecommunications Advancement Foundation, Open Collaborative Research Program at National Institute of Informatics Japan (NII), Japan-China Scientific Cooperation Program (6171101454), and International Exchange Program of National Institute of Information and Communications (NICT), and Fundamental Research Developing Association for Shipbuilding and Offshore.
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Lu, H., Uemura, T., Wang, D. et al. Deep-Sea Organisms Tracking Using Dehazing and Deep Learning. Mobile Netw Appl 25, 1008–1015 (2020). https://doi.org/10.1007/s11036-018-1117-9
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DOI: https://doi.org/10.1007/s11036-018-1117-9