WasteNet: Waste Classification at the Edge for Smart Bins

TL;DR

WasteNet is a convolutional neural network model designed for real-time waste classification on low-power edge devices, achieving 97% accuracy to improve smart bin efficiency and recycling accuracy.

Contribution

The paper introduces WasteNet, a novel low-power CNN model for edge-based waste classification, enabling fast, accurate sorting without cloud dependency.

Findings

Achieves 97% accuracy on waste classification

Operates efficiently on low-power devices like Jetson Nano

Helps reduce recycling contamination

Abstract

Smart Bins have become popular in smart cities and campuses around the world. These bins have a compaction mechanism that increases the bins' capacity as well as automated real-time collection notifications. In this paper, we propose WasteNet, a waste classification model based on convolutional neural networks that can be deployed on a low power device at the edge of the network, such as a Jetson Nano. The problem of segregating waste is a big challenge for many countries around the world. Automated waste classification at the edge allows for fast intelligent decisions in smart bins without needing access to the cloud. Waste is classified into six categories: paper, cardboard, glass, metal, plastic and other. Our model achieves a 97\% prediction accuracy on the test dataset. This level of classification accuracy will help to alleviate some common smart bin problems, such as recycling contamination, where different types of waste become mixed with recycling waste causing the bin to be contaminated. It also makes the bins more user friendly as citizens do not have to worry about disposing their rubbish in the correct bin as the smart bin will be able to make the decision for them.