Smart E-Waste Management System Utilizing Internet of Things and Deep Learning Approaches

Daniel Voskergian
Isam Ishaq
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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 en-vironment. 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 Learn-ing (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 re-sults 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 sustaina-bility. By realizing an intelligent green city vision, we can tackle various contam-ination problems, benefiting both humans and the environment.