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先进的数据增强技术来增强用于建筑和拆除废物管理的实例细分数据集

Birat Gautam¹, Mehrdad Arashpour¹

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

Waste management (New York, N.Y.)

|March 24, 2025

概括

数据增强显著改善了建筑和拆除废物的实例细分. 诸如阶级平衡和真实/合成数据等技术可以提高面具的准确性,特别是在少数阶级.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
废物管理废物管理

背景情况:

数据注释是创建实例细分数据集的一个主要挑战,特别是在建筑和拆除废物方面.
数据增强可以增强数据集的多样性和复杂性,但其应用,例如细分稳定性,尚未得到充分探索.

研究的目的:

开发和评估数据增强技术,以改进实例细分模型.
解决废物管理数据集数据注释的瓶问题.

主要方法:

在公共实例细分数据集上评估各种数据增强技术.
应用类平衡和结合真实/合成数据培训策略.

主要成果:

通过使用类平衡方法实现了面具预测准确度的6%增加.
将真实和合成数据结合起来,使面具预测准确度提高了4%.
少数阶级的口罩预测准确度在增强时大幅增加了30%.

结论:

数据增强技术有效地提高了废物管理环境中的实例细分性能.
提出的方法为废物管理中的各种实例细分数据集提供了适应性.
通过增强解决数据稀缺问题对于强大的废物管理人工智能模型至关重要.

关键词:

增强增强是一种增强.建筑和拆除废物建筑和拆除废物实例分割实例细分实例细分堆叠堆叠在堆叠堆叠综合数据综合数据

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