Smart e-waste management system utilizing Internet of Things and Deep Learning approaches
Daniel Voskergian, Isam Ishaq
Abstract
Electronic waste is presently acknowledged as the rapidly expanding waste stream on a global scale. Consequently, e-waste represents a primary global concern in modern society since electronic equipment contains hazardous substances, and if not managed properly, it will harm human health and the environment. Thus, the necessity for more innovative, safer, and greener systems to handle e-waste has never been more urgent. To address this issue, a smart e-waste management system based on the Internet of Things (IoT) and Deep Learning (DL) based object detection is designed and developed in this paper. Three state-of-the-art object detection models, namely YOLOv5s, YOLOv7-tiny and YOLOv8s, have been adopted in this study for e-waste object detection. The results demonstrate that YOLOv8s achieves the highest mAP@50 of 72% and map@50-95 of 52%. This innovative system offers the potential to manage e-waste more efficiently, supporting green city initiatives and promoting sustainability. By realizing an intelligent green city vision, we can tackle various contamination problems, benefiting both humans and the environment.