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使用交互式人工智能优化废物处理:使用计算机视觉快速引导建筑和拆除废物的细分

Diani Sirimewan¹, Nilakshan Kunananthaseelan², Sudharshan Raman³

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

Waste management (New York, N.Y.)

|September 25, 2024

概括

本研究引入了用于建筑和拆除废物 (CDW) 识别的用户导向细分管道. 该方法改善了材料回收设施中的自动废物识别,促进了资源回收.

科学领域:

废物管理废物管理
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

优化建筑和拆除废物 (CDW) 处理对于资源回收至关重要.
使用图像细分的自动废物识别是关键,但与通用模型面临的领域差距.
当前的方法在混乱的环境中难以识别CDW.

研究的目的:

开发一个用户引导的细分管道,解决CDW识别领域的缺口.
提高材料回收设施中自动化废物识别的准确性和效率.
通过更好的废物监测和处理,加强资源回收.

主要方法:

开发了一个用户引导的细分管道,利用提示 (边界框,点,文本).
在混乱的环境中应用管道来分割CDW.
根据最先进的细分算法评估性能.

主要成果:

在几个CDW类别中实现了大约70%的类别智能性能.
它的性能平均比最先进的算法高出9%.
通过直观的用户提示来减少注释时间.

结论:

用户引导的管道有效地弥合了CDW识别的域差距.
将其整合到材料回收设施中可以加强废物处理和资源回收.
这种人机系统为准确的废物细分提供了一种用户友好的方法.

关键词:

自动废弃物识别自动化废弃物识别自动化自动化自动化自动化自动化计算机视觉计算机视觉计算机视觉建筑和拆除废弃物的废弃物.快速指导的细分是指导的.废物监测和分类废物监测和分类

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