基于深度学习的环境管理模型:识别野生建筑,装修和拆除废物
Diani Sirimewan1, Milad Bazli2, Sudharshan Raman3
1Department of Civil Engineering, Monash University, Melbourne, Australia.
Journal of environmental management
|January 3, 2024
概括
深度学习模型现在可以在现实条件下识别建筑垃圾,改进自动分类. 这一进步有助于建筑行业的资源回收和环境管理.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
背景情况:
- 建筑,改造和拆除 (CRD) 活动产生大量固体废物,有助于填埋负担和污染.
- 当前的废物管理面临着劳动密集型分类和各种废物形式的挑战.
- 深度学习 (DL) 对自动废物识别有希望,但在CRD领域的应用有限.
研究的目的:
- 为了解决DL在CRD废物识别中的有限应用.
- 为了切实地捕捉CRD废物流在不受控制的环境中的复杂性.
- 评估用于野生CRD废弃物识别的最新DL模型的性能.
主要方法:
- 在现实,不受控制的环境中收集和注释CRD废物图像.
- 评估各种预训练DL模型用于特征提取和转移学习.
- 对DL模型自动化CRD废弃物识别的性能进行评估.
主要成果:
- 使用大型和轻量级网络的DL模型在不受控制的环境中有效识别CRD废物流.
- 这些模型展示了自动废物分类的能力.
- 通过预先训练的网络进行转移学习对于CRD废弃物识别是有效的.
结论:
- DL模型适用于在现实条件下识别和分类CRD固体废物.
- 这项研究支持自动化废物分类,增强资源回收.
- 这些发现有助于改善建筑行业的环境管理.
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