应用半监督学习方法用于路面缺陷检测
Peng Cui1,2, Nurjihan Ala Bidzikrillah1, Jiancong Xu3
1School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
本研究介绍了一种半监督式学习方法,使用ResNet-18从道路图像中检测路面缺陷. 热图分析提高了道路表面质量监测的分类准确性.
科学领域:
- 计算机科学 计算机科学
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
背景情况:
- 道路表面质量对于驾驶员的安全和舒适至关重要.
- 由于缺陷多样性和复杂的环境条件,对路面缺陷的实时检测具有挑战性.
研究的目的:
- 开发一个强大的算法来分类和检测路面缺陷.
- 通过半监督学习和图像分析改进道路表面质量监测.
主要方法:
- 采用半监督式学习方法与ResNet-18用于图像特征提取.
- 用于正常路面图像的多变量正常分布模型的单类分类.
- 应用Mahalanobis距离用于缺陷检测和接收器操作特征曲线用于值校准.
主要成果:
- 该模型通过扩展和增强数据实现了从0.868到0.887的改进分类准确性.
- 热图为网络决策提供了洞察力,指导了性能改进.
- 马哈拉诺比斯距离有效地区分了正常和缺陷的路面图像.
结论:
- 半监督学习与ResNet-18相结合,为路面缺陷检测提供了一个有希望的方法.
- 热图可视化有助于理解和提高缺陷检测模型的性能.
- 通过先进的图像分析技术,可以实现准确的道路表面监测.
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