A Novel Application of Deep Learning in a Robotic Arm for the Classification and Sorting of Recyclables
Angela (Zi Tian) Zhou
Magee Secondary
Floor Location : M 092 N

Currently, the world disposes 1.3 billion tonnes of waste per year, with a projected increase to 2.2 billion tonnes by 2025 (World Bank, 2018). Out of the total waste produced in Canada, only around 27% is diverted. The other 73% ends up in either landfills or incinerators, both of which increase pollution, while damaging public health. However, statistics show that around 75% of waste can be diverted in one way or another (United States Environmental Protection Agency, 2018), with recycling accounting for a huge chunk of said diversion. It is quite apparent that the solution to our waste disposal problem is to have better recycling and waste-sorting facilities.

In order to improve the sorting of waste, I made a robotic arm. Transfer learning with AlexNet was used to train the arm to recognize different objects and sort them into separate bins, based on the type of recycling treatment they need. As of now, nobody has applied deep learning into this field in the manner I am proposing. I consulted industry experts from major recyclable treatment companies such as GEEP Canada and Emterra Group, leading recycling technology companies such as MSS Optical Sorters (U.S.), as well as waste-management scientists, to discuss the viability of incorporating this invention into existing recycling processes. Their feedback included many potential uses of my arm, such as reducing the number of workers exposed to health hazards, increasing the efficiency of pre-sorting, reducing contamination after sorting, and classifying e-waste. In general, their responses were positive and encouraging.