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基于视觉的局部建筑和拆除废物的体积估计

Ashwani Jaiswal¹, Kunal Jha¹, Nikhil Bugalia¹

¹Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India.

Waste management (New York, N.Y.)

|August 5, 2025

概括

本研究引入了一种新的基于视觉的框架,用于使用消费者摄像头准确估计建筑和拆除废弃物 (CDW) 的数量. 自动化系统有效量化小型,本地化的CDW库存,以改善反向供应链运营.

科学领域:

环境工程环境工程
计算机视觉计算机视觉
供应链管理供应链管理

背景情况:

精确量化局部建筑和拆除废物 (CDW) 对于优化上游反向供应链 (RSC) 操作至关重要.
现有的CDW量化方法往往是大规模的,半自动化的,并且依赖于昂贵的设备,这使得它们不适合小型的,局部化的库存.
为了有效的城市废物管理,需要经常估计分散的CDW.

研究的目的:

开发和验证一种基于视觉的新框架,用于小型,本地化CDW的自动化,快速和准确的体积估计.
解决城市环境中分散CDW量化的现有方法的局限性.
为上游CDW反向供应链运营提供决策的实用工具.

主要方法:

一种混合细分技术,结合了修改后的RANSAC算法用于地面平面识别和集群过程.
开发使用ResNet-50架构进行CDW集群识别的多视图分类模型 (MVCM).
使用Delaunay三角化方法识别的CDW集群的体积估计.

主要成果:

该框架在3500张图像上使用MVCM识别CDW,获得了0.97的高F1得分.
与手动测量相比,体积估计显示出高精度,绝对百分比误差 (APE) 为8.97%.
端到端处理时间为11分钟,突出了框架在现场部署方面的效率.

结论:

拟议的基于愿景的框架为量化本地化CDW提供了一个自动化,高效和准确的解决方案.
这种技术对于上游的RSC操作非常实用,可以更好地管理分散的城市废物.
该研究使用实验室和现场环境的广泛数据集验证了框架的有效性.

关键词:

建筑和拆除废物的废弃物.深度学习是一种深度学习.混合细分化的混合细分化.一个点云点云.量化量化量化量化逆向供应链是逆向的供应链.

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