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电工建筑垃圾的自动分类:计算机视觉用于部分级别的细分

Aseni Senanayake¹, Mehrdad Arashpour¹

¹Department of Civil Engineering, Monash University, Melbourne, Australia.

Waste management (New York, N.Y.)

|May 20, 2025

概括

使用计算机视觉自动识别电气建筑废物 (ECW) 改善了回收. 斯温变压器模型实现了优越的零件级别细分,增强了可持续废物管理中的资源回收.

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:

全球建筑,拆除和翻新 (CDR) 废物产量正在增加,需要先进的回收解决方案.
由于废物流异质,电气建筑废物 (ECW) 的回收是复杂的,这给自动分类带来了挑战.
目前的细分模型缺乏ECW的部分级识别功能,造成了显著的性能差距.

研究的目的:

开发和评估计算机视觉 (CV) 模型,用于自动化,部分级别的细分和ECW组件的识别.
解决现有模型在处理ECW的复杂性方面的局限性.
提高ECW材料识别的效率,以改善资源回收.

主要方法:

收集和注释特定于ECW的部分级图像.
开发CV模型,包括卷积神经网络 (CNN) 和变压器骨干,用于ECW细分.
评估模型性能,使用精确,部分级别识别的指标.

主要成果:

与CNN相比,Swin变压器模型在部分级ECW细分方面表现出优异的性能.
斯温变压器比ResNet取得了8.60%的改进,比ResNeXt取得了3.64%的改进.
该研究证实了部分级细分的可行性,以实现准确的ECW材料识别.

结论:

使用像Swin变压器这样的先进CV模型进行部分级细分对于ECW识别是实用且有效的.
提高ECW的认可对于提高资源回收和推进可持续废物管理实践至关重要.
这项研究有助于自动化复杂废物流的回收,促进循环经济.

关键词:

计算机视觉计算机视觉计算机视觉电气工程废弃物电气工程废弃物部分级别细分的部分级别细分.废物管理废物管理

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