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On 26th October 2016 our FishFace project was announced winner in the popular vote for the 2016 Google Impact Challenge: Australia!
This provided us with $750,000 to develop our game-changing technology that will protect global fish stocks, the livelihoods of coastal communities and provide a sustainable food source for billions of people.
Tens of thousands of people from all over the world voted for FishFace which got us across the line and we thank everyone for their support and interest in creating sustainable fisheries for people and nature.
What is FishFace?
The world is running out of fish. Global peak fish catch occurred in the 1980s and the global catch has been declining ever since. In fact, 64% of fisheries are now overfished and 90% of all fisheries have no effective management in place. The reason? Insufficient data. We simply don’t know which species are being caught where and in what quantities to inform sustainable management.
Rapidly rising demand combined with falling fish stocks risks a fisheries crisis, which would be a planetary disaster: one in 12 people on Earth depend on fisheries and aquaculture for their livelihood, and three billion people rely on seafood as their primary source of animal protein.
The Nature Conservancy hopes to make a massive positive difference for global fisheries by developing a system we call FishFace to collect, organise, share and utilise the data essential for sustainable fisheries management. It will reduce overfishing and sustain the livelihoods of coastal communities all around the world.
FishFace will use facial recognition technology to automate the collation, at sea, of information on the species and numbers of fish caught, and use this data to inform management decisions. FishFace will be trialed initially in Indonesia’s deep-water snapper and grouper fisheries with the potential to be rolled out for fisheries everywhere.
The machine learning engine that powers FishFace is being developed by Refind Technologies. Refind is a Swedish company providing intelligent sorting solutions using machine vision and deep learning. Refind’s aim is to reduce waste through automation, not only in the seas but also in the used electronics industry.