OREANDA-NEWS. So NVIDIA engineer Robert Bond is using deep learning — and our Jetson TX1 development platform — to recognize cats and turn on his home’s sprinkler system to gently shoo the visitors away.

“My wife is a gardener and she likes her garden to be tidy and clean,” says Bond, 65, a system software engineer who has been at NVIDIA for more than eight years.

Bond quickly dismissed the idea of trapping the cats — which just seemed “unneighborly” — and decided to go with a more technical solution.

Bond is no stranger to deep learning or Jetson. Last year, he built a system that shines a harmless 5-milliwatt laser beam on the ants that occasionally scuttle across his kitchen floor (see “How One NVIDIA Built a Jetson-Powered Laser ‘Ant Annoyer’”).

To train this software, Bond ran as many images of cats as he could through a desktop system running an NVIDIA GeForce GTX TITAN graphics card. At first, the system mistook his shadow for a cat — resulting in Bond getting soaked when he ventured into the yard.

Eventually, it learned to detect cats with increasing accuracy. And because FCN is what’s known as a segmentation network, the system not only identifies cats, it identifies their location in his yard (more on why in a moment).

Once a cat is detected, the deep learning software sends a wireless signal to a relay and a Particle Photon board — a development kit popular with makers — he soldered onto the sprinkler system’s irrigation control box. And the waterworks begin.

The next challenge Bond has set for himself: using the FCN’s ability to not just detect cats but detect their location, to send a remote-controlled car out to shoo the critters away. That would certainly be more fun. But it probably won’t be necessary.

Within just a few days of completing his project, Bond reports that the neighborhood cats began avoiding his yard — and the surprise that awaits — after being squirted one too many times by his home-brewed system.