Facial recognition software is most commonly known as a tool to help police identify a suspected criminal by using machine learning algorithms to analyze his or her face against a database of thousands or millions of other faces. The larger the database, with a greater variety of facial features, the smarter and more successful the software becomes – effectively learning from its mistakes to improve its accuracy.
Now, this type of artificial intelligence is starting to be used in fighting a specific but pervasive type of crime – illegal fishing. Rather than picking out faces, the software tracks the movement of fishing boats to root out illegal behavior. And soon, using a twist on facial recognition, it may be able to recognize when a boat’s haul includes endangered and protected fish.
The latest effort to use artificial intelligence to fight illegal fishing is coming from New York-based The Nature Conservancy (TNC), whichlaunched a contest on Kaggle – a crowdsourcing site that uses competitions to advance data science –earlier this week. TNC hopes the winning team will write software to identify specific species of fish. The program will run on cameras, called electronic monitors, which are installed on fishing boats and used for documenting the catch. The software will put a marker at each point in the video when a protected fish is hauled in. Inspectors, who currently spend up to six hours manually reviewing a single 10-hour fishing day, will then be able to go directly to those moments and check a fishing crew’s subsequent actions to determine whether they handled the bycatch legally – by making best efforts to return it to the sea unharmed.
TNC expects this approach could cut review time by up to 40% and increase the monitoring on a boat. Today, regulators cannot effectively police the actions of these fleets, and only 2% of roughly 1,200 tuna longliner fishing boats in the Western and Central Pacific are required to have government-approved auditors on board. As a result, fishermen sometimes keep protected fish that they hook – including sharks that are killed for their lucrative fins.
In the Pacific’s $7bn tuna fishery, illegal, unreported and unregulated (IUU) fishing not only harms fragile fish stocks, it takes an economic toll ofup to $1.5bn. The impact shows up many ways, including lost income for fishermen in the legal marketplace and harm to the tourist economy that sells snorkelers and divers the opportunity to witness protected species in the wild.
Worldwide, cost estimates related to IUU reach$23bn annually, and the take represents up to 20% of all seafood. Using technology to track and prevent illegal fishing presents an opportunity for technology companies as the fishing industry seeks ways to comply with the growing demand for transparency from governments and consumers.
“If using facial recognition software to track fish were easy, we’d already be using it,” says Matthew Merrifield, TNC’s chief technology officer. Whereas images from security cameras installed inside banks or other buildings are consistent and predictable, “the data from (electronic monitoring) cameras on boats is dirty, because the ships are always moving and the light keeps changing”.
Because of the “dirty” data, it will not be easy to write a facial recognition software that can accurately spot protected species when the variable conditions on the high seas could lead to blurry images on the video.
Given those challenges, it’s too early to know how large this market will grow, or how quickly. While the use of artificial intelligence to reduce illegal catch is relatively new, the Kaggle contest isn’t the first time it is being applied to the fishing industry.
San Francisco-based startup Pelagic Data Systems (PDS) has developed technology that illuminates the activity of some of the 4.6m small-scale commercial fishing boats that ply coastal waters around the world.
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