使用RGB数据的深度学习和回归模型对WEEE在分类厂的自动化材料流特性
Malte Vogelgesang1, Victor Kaczmarek1, Alice do Carmo Precci Lopes2
1Fraunhofer Research Institution for Materials Recycling and Resource Strategies IWKS, Brentanostrasse 2a 63755 Alzenau, Germany.
Waste management (New York, N.Y.)
|May 27, 2025
概括
本研究引入了一种基于传感器的新方法,用于分析废弃电气和电子设备 (WEEE). 该方法使用RGB摄像头和深度学习来准确确定碎碎的WEEE的材料组成,提高回收效率.
科学领域:
- 循环利用和废物管理
- 传感器技术 传感器技术
- 人工智能的人工智能
背景情况:
- 电气和电子设备 (WEEE) 的废物是一个日益严重的全球挑战.
- 回收WEEE对于回收有价值和关键的原材料至关重要.
- 目前的手动采样和排序用于WEEE分析是劳动和成本密集的.
研究的目的:
- 开发一种基于传感器的自动化材料流特性 (SBMC) 方法,用于切碎的WEEE.
- 通过精确的材料流组成分析,优化WEEE回收过程.
- 克服现有的SBMC方法的局限性,尚未应用于切碎的EEE.
主要方法:
- 开发了一种三步SBMC方法,使用RGB摄像头对切碎的WEEE进行分析.
- 利用YOLO v11深度学习进行材料识别 (金属,塑料,PCB).
- 采用k-最近邻居回归用于粒子质量预测和材料流组成分析.
主要成果:
- 在材料识别方面,YOLO v11实现了0.990的mAP@0.5.
- K-最近邻居回归预测的粒子质量,平均相对误差低于5%.
- 结合YOLO和k-NN方法在验证数据集上实现了4.94%的错误.
结论:
- 开发的SBMC方法为分析碎碎的WEEE提供了准确和自动化的解决方案.
- 这项技术可以加强WEEE分类厂的监测和控制.
- 该方法有助于有效地从电子垃圾中回收有价值的原材料.
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