一种基于深度学习的混合分类和评估方法,用于用于回收装饰和装修废物的回收回收
Pujin Wang1, Jianzhuang Xiao2, Ruoyu Liu3
1College of Civil Engineering, Tongji University, Shanghai 200092, China.
Waste management (New York, N.Y.)
|November 5, 2024
概括
回收建筑和装修废物是具有挑战性的,因为其成分不明. 本研究引入了一种混合深度学习和机器学习方法,以准确评估废物成分和重量,提高回收效率.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 中国的快速城市化产生了大量的装饰和装修废物 (D&RW).
- 由于难以准确评估其成分,因此阻碍了D&RW的有效回收.
- 了解D&RW的组成和质量对于回收高价值材料至关重要.
研究的目的:
- 开发一种用于分类和评估D&RW的自动化方法.
- 加强从D&RW.W.中提升有价值组件的回收.
- 为应对日益增长的D&RW量所带来的挑战.
主要方法:
- 一种混合方法,结合实例细分深度学习 (DL) 和形态机器学习 (ML) 模型.
- 为了培训和验证,利用了53,000个个粒的精心标记的数据集.
- ML模型基于重量评估的形态数据向量来预测等效粒厚.
主要成果:
- 混合DL-ML模型准确地对D&RW进行分类和评估.
- 与手动分类相比,在总重量评估中达到2.8%的低相对误差.
- 证明了D&RW组成和重量的自动评估.
结论:
- 拟议的混合方法为D&RW评估提供了准确和高效的解决方案.
- 自动化分类和评估过程可以显著改善D&RW回收.
- 这种方法有助于从建筑和装修废物中更好地回收高价值材料.
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