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Open AccessArticle

Sharkeye: Real-Time Autonomous Personal Shark Alerting via Aerial Surveillance

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SMART Infrastructure Facility, University of Wollongong, Wollongong 2522, Australia
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Centre for Sustainable Ecosystem Solutions and School of Earth Atmospheric and Life Sciences, University of Wollongong, Wollongong 2522, Australia
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Across the Cloud Pty Ltd., Wollongong 2500, Australia
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Centre for Bioinformatics and Biometrics, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong 2522, Australia
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Sharkmate, Wollongong 2500, Australia
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Advanced Multimedia Research Lab, University of Wollongong, Wollongong 2522, Australia
*
Author to whom correspondence should be addressed.
Drones 2020, 4(2), 18; https://doi.org/10.3390/drones4020018
Received: 16 March 2020 / Revised: 23 April 2020 / Accepted: 24 April 2020 / Published: 4 May 2020
(This article belongs to the Special Issue Drone Technology for Wildlife and Human Management)
While aerial shark spotting has been a standard practice for beach safety for decades, new technologies offer enhanced opportunities, ranging from drones/unmanned aerial vehicles (UAVs) that provide new viewing capabilities, to new apps that provide beachgoers with up-to-date risk analysis before entering the water. This report describes the Sharkeye platform, a first-of-its-kind project to demonstrate personal shark alerting for beachgoers in the water and on land, leveraging innovative UAV image collection, cloud-hosted machine learning detection algorithms, and reporting via smart wearables. To execute, our team developed a novel detection algorithm trained via machine learning based on aerial footage of real sharks and rays collected at local beaches, hosted and deployed the algorithm in the cloud, and integrated push alerts to beachgoers in the water via a shark app to run on smartwatches. The project was successfully trialed in the field in Kiama, Australia, with over 350 detection events recorded, followed by the alerting of multiple smartwatches simultaneously both on land and in the water, and with analysis capable of detecting shark analogues, rays, and surfers in average beach conditions, and all based on ~1 h of training data in total. Additional demonstrations showed potential of the system to enable lifeguard-swimmer communication, and the ability to create a network on demand to enable the platform. Our system was developed to provide swimmers and surfers with immediate information via smart apps, empowering lifeguards/lifesavers and beachgoers to prevent unwanted encounters with wildlife before it happens. View Full-Text
Keywords: UAV; blimp; shark spotting; machine learning; wearables UAV; blimp; shark spotting; machine learning; wearables
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Gorkin, R., III; Adams, K.; Berryman, M.J.; Aubin, S.; Li, W.; Davis, A.R.; Barthelemy, J. Sharkeye: Real-Time Autonomous Personal Shark Alerting via Aerial Surveillance. Drones 2020, 4, 18.

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