基于深度度度度学习的分类,用于路面困境图像

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Yuhui Li^1,2, Jiaqi Wang³, Bo Lü³

¹School of Physics, Northeast Normal University, Changchun 130024, China.

Sensors (Basel, Switzerland)

|July 12, 2025

概括

本研究引入了用于路面危险分类的深度度度度学习方法,提高了准确性,并使道路检查的增量学习成为可能. 该方法通过有效地识别各种各样的路面问题,以有限的数据来增强道路维护.

科学领域:

土木工程土木工程是指土木工程.
计算机科学计算机科学
人工智能的人工智能

背景情况:

传统的路面危险分类方法与大量数据集要求和学习新类别的能力有限而扎.
在人行道危险图像中,高类内差异和低类间差异对准确的自动化分析构成重大挑战.

研究的目的:

开发一种基于深度度度量学学习的新方法,用于路面危险分类.
克服现有方法的局限性,包括数据依赖和增量学习.
通过改进应急识别,提高道路检查的准确性和效率.

主要方法:

设计了一个卷积神经网络 (CNN) 头部,使用SoftTriple损失训练的多集群中心体.
通过结合样本相似性和类先验来解决数据不平衡,采用了自适应权重策略.
使用软标签技术,通过评估与支持集样本的相似性来减少标签噪声.

主要成果:

与传统的监督学习相比,拟议的方法在UAV-PDD2023数据集上取得了更高的性能.
显示了比监督学习方法高3.2%的宏观回忆率.
与iCaRL增量学习相比,宏观F1和加权F1分数分别提高了6.7%和8.5%.

结论:

开发的深度度度度学习方法有效地分类了人行道困境,即使有很高的类内差异和很低的类间区别.
该方法能够逐步学习新类别并处理数据不平衡的能力使其适合于现实世界的道路检查.
这项研究为不断变化的路面危险类型和局限注释的场景提供了强大的解决方案,推进了自动化道路维护.

关键词:

软三倍损失三倍损失深度度度学学习 (deep metric learning) 是一种深度度度学.图像的分类图像的分类.路面应急检测路面应急检测类似度指标是相似度指标.

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