Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, People's Republic of China.
Department of Civil Engineering, College of Science, Nanjing University of Science and Technology, Nanjing, Xuanwu District 210094, People's Republic of China.
Philos Trans A Math Phys Eng Sci. 2023 Sep 4;381(2254):20220165. doi: 10.1098/rsta.2022.0165. Epub 2023 Jul 17.
The three-dimensional detection in point cloud data for pavement cracks has drawn the attention of many researchers recently. In the field of pavement surface point cloud detection, the key tasks include the identification of pavement cracks and the extraction of the location and size information of pavement cracks. Based on the point cloud data of pavement surface, we developed two methods to directly extract and detect cracks, respectively. The first method is based on the improved sliding window algorithm by combining the random sample consensus (RANSAC) technique to directly extract the crack information from point clouds. The second method is developed based on YOLOv5 to process the two-dimensional images transformed from point cloud data for automatic pavement crack detection. We also attempted to fuse the point cloud images with greyscale images as input for the YOLOv5. Analysis results show that the improved sliding window algorithm efficiently extracts pavement cracks with less noise, and the YOLOv5-based method obtains a good detection of pavement cracks. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
近年来，路面裂缝的点云数据三维检测引起了许多研究人员的关注。在路面表面点云检测领域，关键任务包括识别路面裂缝和提取路面裂缝的位置和大小信息。基于路面表面的点云数据，我们分别开发了两种直接提取和检测裂缝的方法。第一种方法是基于改进的滑动窗口算法，结合随机抽样一致性（RANSAC）技术，直接从点云中提取裂缝信息。第二种方法是基于 YOLOv5 开发的，用于处理从点云数据转换而来的二维图像，以实现自动路面裂缝检测。我们还尝试将点云图像与灰度图像融合作为 YOLOv5 的输入。分析结果表明，改进的滑动窗口算法能够有效地提取出噪声较小的路面裂缝，而基于 YOLOv5 的方法则能够很好地检测路面裂缝。本文是“交通基础设施和材料失效分析中的人工智能”主题的一部分。
