A Smart Recycling Bin Using Waste Image Classification At The Edge

TL;DR

This paper presents a smart recycling bin with high-accuracy waste classification using edge AI models on Jetson Nano and K210, achieving energy efficiency and potential for urban waste management improvement.

Contribution

Developed a novel smart recycling bin prototype with optimized waste classification models for edge devices, demonstrating high accuracy and low power consumption.

Findings

Model accuracy of 95.98% on Jetson Nano and 96.64% on K210.

Power consumption reduced by 30% on Jetson Nano to 4.7 W.

Energy-efficient prototype suitable for commercialization.

Abstract

Rapid economic growth gives rise to the urgent demand for a more efficient waste recycling system. This work thereby developed an innovative recycling bin that automatically separates urban waste to increase the recycling rate. We collected 1800 recycling waste images and combined them with an existing public dataset to train classification models for two embedded systems, Jetson Nano and K210, targeting different markets. The model reached an accuracy of 95.98% on Jetson Nano and 96.64% on K210. A bin program was designed to collect feedback from users. On Jetson Nano, the overall power consumption of the application was reduced by 30% from the previous work to 4.7 W, while the second system, K210, only needed 0.89 W of power to operate. In summary, our work demonstrated a fully functional prototype of an energy-saving, high-accuracy smart recycling bin, which can be commercialized in the future to improve urban waste recycling.