Machine Learning Does Its Civic Duty By Spotting Roadside Litter

If there’s one thing that never seems to suffer from supply chain problems, it’s litter. It’s everywhere, easy to spot and — you’d think — pick up. Sadly, most of us seem to treat litter as somebody else’s problem, but with something like this machine vision litter mapper, you can at least be part of the solution.

For the civic-minded [Nathaniel Felleke], the litter problem in his native San Diego was getting to be too much. He reasoned that a map of where the trash is located could help municipal crews with cleanup, so he set about building a system to search for trash automatically. Using Edge Impulse and a collection of roadside images captured from a variety of sources, he built a model for recognizing trash. To find the garbage, a webcam with a car window mount captures images while driving, and a Raspberry Pi 4 runs the model and looks for garbage. When roadside litter is found, the Pi uses a Blues Wireless Notecard to send the GPS location of the rubbish to a cloud database via its cellular modem.

Cruising around the streets of San Diego, [Nathaniel]’s system builds up a database of garbage hotspots. From there, it’s pretty straightforward to pull the data and overlay it on Google Maps to create a heatmap of where the garbage lies. The video below shows his system in action.

Yes, driving around a personal vehicle specifically to spot litter is just adding more waste to the mix, but you’d imagine putting something like this on municipal vehicles that are already driving around cities anyway. Either way, we picked up some neat tips, especially those wireless IoT cards. We’ve seen them used before, but [Nathaniel]’s project gives us a path forward on some ideas we’ve had kicking around for a while.

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Automate The Freight: The Robotic Garbage Man

When I started the Automate the Freight series, my argument was that long before the vaunted day when we’ll be able to kick back and read the news or play a video game while our fully autonomous car whisks us to work, economic forces will dictate that automation¬†will have already penetrated the supply chain. There’s much more money to be saved by carriers like FedEx and UPS cutting humans out of the loop while delivering parcels to homes and businesses than there is for car companies to make by peddling the comfort and convenience of driverless commuting.

But the other end of the supply chain is ripe for automation, too. For every smile-adorned Amazon package delivered, a whole bunch of waste needs to be toted away. Bag after bag of garbage needs to go somewhere else, and at least in the USA, municipalities are usually on the hook for the often nasty job, sometimes maintaining fleets of purpose-built trucks and employing squads of workers to make weekly pickups, or perhaps farming the work out to local contractors.

Either way you slice it, the costs for trash removal fall on the taxpayers, and as cities and towns look for ways to stretch those levies even further, there’s little doubt that automation of the waste stream will start to become more and more attractive. But what will it take to fully automate the waste removal process? And how long before the “garbage man” becomes the “garbage ‘bot”?

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