对估计建筑废弃物成分的三种方法进行比较分析
Yiman Jiang1, Ruoxin Wang2, Dongxing Xuan3
1Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China; School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW, 2751, Australia.
Waste management (New York, N.Y.)
|January 17, 2025
概括
手动分类对于建筑垃圾来说是具有成本效益的,但图像识别方法对于惰性垃圾提供了可比的准确性和潜在的长期效率增长.
科学领域:
- 环境科学 环境科学
- 资源管理 资源管理
- 废物管理 废物管理
背景情况:
- 精确的建筑废弃物成分确定对于有效的资源管理和回收设施设计至关重要.
- 现有的废物分类和识别方法在特定场景中存在局限性.
研究的目的:
- 为了比较手动分类,手动图像识别 (红外热成像) 和基于深度学习的图像识别 (SegFormer) 用于建筑废弃物成分分析.
- 根据准确性,成本,时间,社会环境影响和改进潜力来评估这些方法.
主要方法:
- 手动分类 (参考方法).手动分类 (参考方法).
- 使用红外热成像进行手动图像识别.
- 基于深度学习的图像识别,使用SegFormer语义细分模型.
主要成果:
- 手动和深度学习方法在惰性废物中都获得了高精度 (误差≤5.14%),但在非惰性废物中显示出更高的误差.
- 手动分类是最快,最具成本效益的,但劳动密集型,具有环境缺点.
- 图像识别方法比手动分类要慢,成本更高,但具有环境效益和未来效率.
结论:
- 尽管手动分类存在局限性,但手动分类在成本和速度方面仍然很实用.
- 图像识别技术有望改善建筑垃圾管理,特别是深度学习的进步.
- 进一步的研究可以优化非惰性废物的图像识别,并将这些技术集成到回收设施中.
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