recycling is proving to be a big challenge for even the world’s largest economies. According to recent data, more than 90 million tons of recyclable material are in United States landfills every year. One of the biggest reasons for this is that items that cannot be recycled are mixed in with the ones that can.
In fact, it is estimated that, on average, 1 in every 4 items in recycling bins is a non-recyclable item. Now, this problem is worse than the numbers may suggest. The US doesn’t have the needed infrastructure to handle recycling efforts on a massive scale, so it relied on China for them. However, this issue of mixing recyclable materials with garbage has become so severe that China started rejecting recycling. This signifies a need for a change in the way recycling is done in the US. One possible solution to the waste issue is using AI robots to enhance our recycling efforts. However, this issue with mixing recyclable materials with garbage has been
In this article, we will look at how AI can improve recycling efforts and the data annotation essential to create a smarter way to recycle.
While there are usually recycling bins next to garbage in many cities around the world and in many offices as well, a lot of people throw their trash in the recycling bins or, vice-versa, items that should be recycled are placed in the garbage. One of the ways recyclables are separated from trash items is through optical sorting. These machines use reflected light to analyze products that have been simulated and presented to the viewing area. This singulation can be achieved utilizing low-friction inclined chutes or conveyor belts. Specially designed optical filters narrow the reflected light into specific wavelengths that accentuate the difference between the acceptable product and the product to be rejected. Specialized CCD, IR, UV, or RGB (full color red/green/blue) cameras receive the reflected light from the product and send this information to be amplified and classified.
