Computer-vision-powered Automatic Waste Sorting Bin: A Machine Learning-based Solution on Waste Management

Abstract

The mainstream waste separation methods - recycle bins and manual separation - are time consuming, prone to error, and human labor intensive. Robots have demonstrated their superior efficiency at repetitive task over human's time to time again. This research implements and evaluate a computer-vision-powered Automatic Waste Sorting Bin, which is capable to classify the waste types in a short time with high efficiency. Controlled and classified using a Raspberry pi and a camera, the bin can detect the waste type and drop it in the right bin accordingly. The dataset on which the model is trained on is relatively small. An image of the waste is first captured with the camera, and then analyzed using a YOLOv5 model. Parameters that yield the optimal result are 150 epochs with a YOLOv5l with an accuracy of 93.33 %. To improve the model's performance, we experimented with different epoch settings and measured the results. The setup proposed with this paper provides an automated solution to replace the mainstream methods of waste separation. This paper provides a low-cost and flexible solution that can be easily replicated and trained with a larger dataset to acquire a better result. Future research may reference this paper as a proof of concept and/or an implementation for a streamlined solution.