Abstract
Shark attacks have been a very sensitive issue for Australians and many other countries. Thus, providing safety and security around beaches is very fundamental in the current climate. Safety for both human beings and underwater creatures (sharks, whales, etc.) in general is essential while people continue to visit and use the beaches heavily for recreation and sports. Hence, an efficient, automated and real-time monitoring approach on beaches for detecting various objects (e.g. human activities, large fish, sharks, whales, surfers, etc.) is necessary to avoid unexpected casualties and accidents. The use of technologies such as drones and machine learning techniques are promising directions in such challenging circumstances. This paper investigates the potential of Region-based Convolutional Neural Networks (R-CNN) for detecting various marine objects, and Sharks in particular. Three network architectures namely Zeiler and Fergus (ZF), Visual Geometry Group (VGG16), and VGG_M were considered for analysis and identifying their potential. A dataset consisting of 3957 video frames were used for experiments. VGG16 architecture with faster-R-CNN performed better than others, with an average precision of 0.904 for detecting Sharks.
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https://taronga.org.au/conservation/conservation-science-research/australian-shark-attack-file/2015
https://taronga.org.au/conservation/conservation-science-research/australian-shark-attack-file/2016
Acknowledgement
The authors would like to thank the Ripper Group for providing the data samples for the experiments. The Ripper Group operates the Westpac Little Ripper Lifesaver UAV/drones at beaches in NSW in conjunction with Surf Life Saving NSW. This research was funded by The Ripper Group under Research Contract with The University of Technology Sydney (UTS).
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Sharma, N., Scully-Power, P., Blumenstein, M. (2018). Shark Detection from Aerial Imagery Using Region-Based CNN, a Study. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_23
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