New shark spotter algorithm saves lives, and sharks
Sharks get a bad rap and it's in part because they eat people, not many and it's statistically insignificant, but nevertheless people don't like being eaten, now though an AI might finally put a stop to them snacking between meals
Key takeaways
- Nabin Sharma, a research associate at the University of Technology Sydney (UTS), the new algorithm is 90 percent accurate in distinguishing sharks from dolphins, rays, whales, and other marine life.
- The Shark Spotter tech launched 12 months ago by the Australia based Westpac Group, a bank that sponsors search and rescue drones.
- Over the course of the year, UTS staff flew drones over coastal waters and captured roughly 8,000 images.
Cite or link to this article
Griffin, M. (2017) 'New shark spotter algorithm saves lives, and sharks', 311 Institute, 8 August. Available at: https://www.311institute.com/new-shark-spotter-algorithm-saves-lives-and-sharks/ (Accessed: 1 October 2026).
Artificial Intelligence (AI) and blockchain are already helping to protect animals around the world, whether it's underwater robots zapping salmon with lasers, creating a Facebook for whales, or protecting the fish in the Pacific, but now a research team in Australia have developed an algorithm capable of using video footage streamed from drones to detect sharks and alert swimmers, and they’ve dubbed it, naturally, “Shark Spotter.”
According to Dr. Nabin Sharma, a research associate at the University of Technology Sydney (UTS), the new algorithm is 90 percent accurate in distinguishing sharks from dolphins, rays, whales, and other marine life. Those numbers are significant when compared to an 18 percent accuracy of human spotters using their naked eye in helicopters, or the 12 percent rate for humans in fixed-wing aircraft.
The Shark Spotter tech launched 12 months ago by the Australia based Westpac Group, a bank that sponsors search and rescue drones. Westpac commissioned the researchers at UTS to develop the algorithm and integrate it with the company’s existing Little Ripper Lifesaver drones. Over the course of the year, UTS staff flew drones over coastal waters and captured roughly 8,000 images. The team then created the algorithm using a deep learning platform.
“The system efficiently distinguishes and identifies sharks from other targets by processing video feeds that are dynamic as well as images where objects are static,” said UTS Prof. Michael Blumenstein.
Using the new technology drones will be able to hover over sharks when they see them and send alerts to nearby lifeguards and emergency services, they’ll also alert any swimmers below using an on board megaphone.
Ben Trollope, the operations manager for Westpac Little Ripper, said there are currently 35 drones in the fleet which range in flight capacity from 15 minutes to four hours and it costs a government or private institution about $40,000 to buy the drone, pay for training for four people, and cover ongoing maintenance.
The Little Ripper Lifesavers are expected to be equipped with the Shark Spotter technology this summer and it’s hoped that they’ll save both human lives, and the lives of sharks, who are normally captured and culled if they decide to take a bite out of someone.
What this means for your organisation
Machines now beat human spotters.
The algorithm is 90 percent accurate. Human spotters managed 18 percent from helicopters and 12 percent from fixed-wing aircraft. Where sight decides the outcome, that gap is hard to ignore.
Detection is only half the job.
The drones hover over a shark, alert lifeguards and emergency services, and warn swimmers by megaphone. The value sits in the response as much as the sighting. Drones sit at the Prepare stage in 311 data, with wide use expected by 2028.
Better data protects people and wildlife.
The team built the algorithm from roughly 8,000 images using a deep learning platform. Sharks that bite someone are normally captured and culled. Telling sharks apart from dolphins, rays and whales helps both sides.
Three questions for your next leadership meeting
- Where do our people still rely on the naked eye for critical calls?
- Who acts on an alert, and how fast, once a machine raises it?
- What proof of accuracy would we need before trusting an algorithm?
The trends and technologies behind this story
From the 311 Starburst, the 311 Strategic Intelligence Unit’s map of the technologies and trends shaping the next 50 years.
Analysis by the 311 Strategic Intelligence Unit, from Matthew Griffin’s article and 311 data. First published on Fanatical Futurist.
FAQ
Why does this matter?
Sharks get a bad rap and it's in part because they eat people, not many and it's statistically insignificant, but nevertheless people don't like being eaten, now though an AI might finally put a stop to them snacking between meals
About the author
Matthew Griffin Founder, President and Futurist, 311 Institute
Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath."
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Matthew Griffin is a multi-award winning Futurist and expert in Disruption and Innovation, Geopolitics, Leadership, and Technology, who NASA have described as a "walking encyclopaedia of the future" and a "futurist Polymath." 17-time best selling author of the "Codex of the Future" series, Matthew is the Founder, President and Futurist of the 311 Institute, a global Futures and Deep Futures advisory firm working with royal households, world leaders, G7, G20, and G77 governments, NGOs, and multi-national mid and mega cap firms to help them explore, shape, and lead the next 50 years of business and society.
An award-winning YouTube creator with over a million followers, with an unrivalled global reach and impact, Matthew is a highly sought-after international keynote speaker, lecturer, and mentor who collaborates with global leaders through the United Nations Alliance of Civilizations (UNAOC) and United Nations General Assembly (UNGA) to shape pivotal initiatives such as the UN’s AI for Humanity programme, the United Nations Conference of the Parties (UN COP), and the World Economic Forum in Davos.
As the former Global Head of Cloud, National Security, and Enterprise Sales for companies including Atos, Dell-EMC, and IBM, Matthew has a proven track record of building multi-billion dollar business units and turning failing divisions into market leaders. His ability to identify, analyse, and communicate the implications of hundreds of emerging technologies and trends is unparalleled, and his insights are trusted by many of the world’s most respected organisations, including ABB, Accenture, Adidas, AON, ARM, BCG, Centrica, Citi, Coca-Cola, Dentons, Deloitte, Dow Jones, EY, Google, KPMG, Lego, Legal & General, LinkedIn, Microsoft, PepsiCo, Qualcomm, RWE, Samsung, Siemens AG and Siemens Energy, T-Mobile, UBS, VISA, Walmart, Workday, Worldpay and many others.
Regularly featured in the global media including the AP, BBC, Bloomberg, CNBC, Discovery, Forbes, Khaleej Times, Telegraph, TIME, ViacomCBS, WIRED, and the WSJ, Matthews mission is to help organisations create a fair and sustainable future whose benefits are shared by everyone irrespective of their ability, background, or circumstances.
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Sources and further reading
- University of Technology Sydney uts.edu.au
Source: first published by the 311 Institute on 8 August 2017. Cite as: Griffin, M. (2017). New shark spotter algorithm saves lives, and sharks. 311 Institute. https://www.311institute.com/new-shark-spotter-algorithm-saves-lives-and-sharks/
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