利用物联网和深度学习方法的智能电子废物管理系统

Daniel Voskergian, I. Ishaq
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引用次数: 0

摘要

电子垃圾目前被认为是全球范围内迅速扩大的废物流。因此,电子废物代表了现代社会的主要全球关注,因为电子设备含有有害物质,如果管理不当,它将危害人类健康和环境。因此,建立更创新、更安全、更环保的系统来处理电子垃圾的必要性从未像现在这样迫切。为了解决这一问题,本文设计并开发了一种基于物联网(IoT)和基于深度学习(DL)的智能电子垃圾管理系统。本研究采用YOLOv5s、YOLOv7-tiny和YOLOv8s三种最先进的目标检测模型进行电子垃圾目标检测。结果表明,YOLOv8s达到最高的mAP@50(72%)和map@50-95(52%)。这一创新系统提供了更有效地管理电子废物、支持绿色城市倡议和促进可持续性的潜力。通过实现智慧的绿色城市愿景,我们可以解决各种污染问题,造福人类和环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart e-waste management system utilizing Internet of Things and Deep Learning approaches
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.
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