用轻量CNN+物联网实现电子垃圾自动分类
Leveraging CNN and IoT for Effective E-Waste Management
- 结合摄像头与称重设备，基于视觉和重量自动识别电子垃圾
- 可实时检测电路板、传感器、导线等部件，提升回收效率
- 适合环保部门与智能回收站部署使用
现代电子设备的普及导致电子垃圾激增。不当处置和回收不足带来严重环境与健康风险。本文提出一种基于物联网的系统，结合轻量级卷积神经网络（CNN）分类流程，提升电子垃圾的识别、分类与分拣效率。通过集成摄像头与数字称重装置，系统依据视觉特征与重量属性实现电子物品的自动化分类。实验表明，该框架能实时检测电路板、传感器、导线等组件，推动智能回收流程，显著提高整体废物处理效率。
原文摘要 · Abstract (English)
The increasing proliferation of electronic devices in the modern era has led to a significant surge in electronic waste (e-waste). Improper disposal and insufficient recycling of e-waste pose serious environmental and health risks. This paper proposes an IoT-enabled system combined with a lightweight CNN-based classification pipeline to enhance the identification, categorization, and routing of e-waste materials. By integrating a camera system and a digital weighing scale, the framework automates the classification of electronic items based on visual and weight-based attributes. The system demonstrates how real-time detection of e-waste components such as circuit boards, sensors, and wires can facilitate smart recycling workflows and improve overall waste processing efficiency.
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