基于点云数据和数据融合的路面裂检测
Qiao Dong1, Sike Wang1, Xueqin Chen2
1Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, People's Republic of China.
概括
研究人员开发了两种人工智能方法来检测3D点云数据中的路面裂. 一个使用RANSAC改进的滑动窗口算法进行提取,而另一个使用YOLOv5从转换的图像进行检测.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 路面裂检测对于基础设施维护至关重要.
- 准确识别和描述裂是关键的挑战.
- 3D点云数据为路面分析提供了丰富的信息.
研究的目的:
- 开发和评估使用点云数据进行3D路面裂检测的新方法.
- 为了提取裂纹信息,包括位置和大小.
- 在运输基础设施分析中推进人工智能应用.
主要方法:
- 开发了一种改进的滑动窗口算法,与随机样本共识 (RANSAC) 结合,用于从点云直接提取裂纹.
- 实现基于YOLOv5的深度学习模型,用于使用从点云数据中转换的2D图像自动检测路面裂.
- 研究的融合点云图像与灰度图像作为YOLOv5.5的输入.
主要成果:
- 改进的滑窗算法有效地提取路面裂,降低噪音.
- 基于YOLOv5的方法在自动路面裂检测方面表现良好.
- 图像类型的融合显示了提高检测准确性的潜力.
结论:
- 两种开发的方法都显示出对3D路面裂分析的前景.
- 由人工智能驱动的方法为人行道健康监测提供了高效和准确的解决方案.
- 这项研究有助于人工智能在运输基础设施故障分析领域的发展.
相关概念视频
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01:20Microcracking in Concrete
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01:28Types of Non-structural Cracks in Concrete
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01:29Preplaced Aggregate Concrete
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01:12Non-destructive Tests for Concrete Strength
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01:21Masonry Paving
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