A deep learning system is helping biologists who survey offshore fish populations to prevent overfishing.

What’s new: The U.S. agency in charge of protecting ocean resources is using an underwater camera and neural network to count fish in real time.

How it works: Alaska’s walleye pollock fishery is America’s largest by volume. (You may not recognize a walleye pollock, but you’ve probably eaten one in fish sticks, fast-food sandwiches, or imitation crab meat. They are delicious!) Scientists with the U.S. National Oceanic and Atmospheric Administration chose this fishery as a pilot in their automatic fish-identification program.

  • NOAA scientists dragged a long, funnel-shaped net through the water. Fish caught in the wide mouth are allowed to escape through the narrow, open end, passing in front of a stereoscopic CamTrawl camera system as they exit.
  • Next to CamTrawl, a computer in a hermetically-sealed container runs a fish-recognition network called Viame. This video shows the user interface in action.
  • The biologists do more than count fish. They also need to know the fishes’ average age to calculate a healthy number for fishermen to catch. Viame triangulates each specimen’s length, a reliable indicator of its age.
  • NOAA is also using Viame to count scallops, reef fish, and endangered seals.

Behind the news: Congress passed the Sustainable Fisheries Act in 1996, requiring NOAA to track U.S. commercial fish populations. For some fisheries, the biologists venture out on boats, casting nets to capture samples of what’s in the water. They dump the contents onto the deck, count and measure each creature, release the haul, and cast the net again. NOAA launched the initiative to automate these counts using artificial intelligence in 2014.

Why it matters: Fish stock assessments, and the limits they impose on commercial fishing, keep fish populations sustainable and fisheries productive over the long term. Automating the process reduces error and frees up biologists for other work.

We’re thinking: Deep learning is producing more and better data for environmental stewardship. It’s up to citizens to put that data to best use.


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