建筑和拆除废物的高效细分和注释:大型视觉基础模型中的空间感知适配器
Birat Gautam1, Saeed Reza Mohandes2, Mehrdad Arashpour1
1Department of Civil Engineering, Monash University, Melbourne, Australia.
Waste management (New York, N.Y.)
|December 12, 2025
概括
这项研究增强了大型视觉基础模型 (LVFMs) 用于建筑和拆除废物 (CDW) 细分,使用适配器和少量学习. 新的框架提高了准确性,并减少了可持续废物管理的注释时间.
科学领域:
- 计算机视觉 计算机视觉
- 可持续工程 可持续工程
- 废物管理 废物管理
背景情况:
- 建筑和拆除废弃物 (CDW) 的利用对可持续性至关重要.
- 目前的CDW分类效率低且劳动密集.
- 现有的计算机视觉模型与动态废物流作斗争,需要大量的数据.
研究的目的:
- 为精确的CDW类特定细分开发一个资源高效的框架.
- 增强大型视觉基础模型 (LVFMs) 以改进废物流分析.
- 引入一个半自动化的注释管道,使用少数拍摄的学习.
主要方法:
- 通过空间意识的适配器架构来增强LVFM.
- 实现一个半自动注释管道,并使用一些射击学习.
- 在一个扩展的数据集上进行实验,包括代表性不足的废物类别.
主要成果:
- 在完全监管的设置中,实现了0.744的欧盟平均交叉点 (IoU),表现比基线高4.5%.
- 在只有20张训练图像的少数拍摄场景中,在"灯光"类中达到0.770的IOU.
- 标注管道产生了高质量的面膜 (IoU 0.883),与手动标注相比,但速度明显更快.
结论:
- 拟议的框架为CDW细分提供了一个可扩展和资源高效的解决方案.
- 这种方法在监督和少量学习环境中显著提高了准确性.
- 这项工作通过增强人工智能驱动的分类来推进可持续的CDW价值化.
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