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提升织品损伤细分:一个新的RGBT数据集和热频率规范化

Farshid Rayhan¹, Jitesh Joshi¹, Guangyu Ren¹

¹Department of Computer Science, University College London, London NW1 2AE, UK.

Sensors (Basel, Switzerland)

|April 12, 2025

概括

本研究介绍了RGBT-Textile,这是一个用于近距离织品损伤细分的新数据集. 一种新的方法,ThermoFreq,通过减少热噪声来提高细分精度,提高在具有挑战性的场景中对象识别.

科学领域:

计算机视觉计算机视觉
材料科学材料科学材料科学
图像处理图像处理

背景情况:

对于高动态范围场景分析而言,RGB-热 (RGBT) 语义细分至关重要.
热成像提供了增强的近距离特征提取,对于织品损伤检测等应用至关重要.

研究的目的:

介绍RGBT-Textile,这是用于近距离织品和损坏细分的专用数据集.
介绍ThermoFreq,一种新的热频规范化方法,用于减轻RGBT细分任务中的温度噪声.

主要方法:

与织科学家一起开发了一个精细的数据收集协议,软件工具和标签过程.
创建了RGBT-Textile数据集,用于近距离的织品和损坏细分.
引入并应用了ThermoFreq方法来减少热噪声.

主要成果:

使用最先进的 (SOTA) 模型评估了RGBT-Textile和其他六个RGBT数据集.
在使用热频方法时,证明了SOTA模型的卓越性能.
证实了ThermoFreq在应对各种环境条件下的噪音挑战方面的有效性.

结论:

RGBT-Textile数据集和ThermoFreq方法显著提升了织应用的近距离RGBT语义细分.
ThermoFreq有效地降低了噪音,在具有挑战性的热成像场景中提高了细分精度.
该数据集的公开发布旨在促进该领域的进一步研究和合作.

关键词:

在RGB-Thermal数据集中,语义细分语义细分语义细分语义细分织品损坏检测检测检测的方法

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