Related Experiment Video
Updated: May 28, 2026

Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight
Published on: May 31, 2022
Applying a Combination of the YOLOv8 Model and 3D Point Cloud Images in Asphalt Pavement Maintenance
Yangyang Wang1,2, Shoujing Yan2, Weibo Shi2
1Department of Architecture and Civil Engineering, Zhejiang University, Hangzhou 310030, China.
Abstract:
Asphalt pavement distress detection plays a pivotal role in highway maintenance, providing an essential basis for optimizing maintenance strategies and allocating funding. Consequently, quick detection and efficient identification of distress are crucial for enhancing the quality of highway maintenance. This study aims to acquire high-precision distress data using 3D laser point cloud technology, identify distress types via the YOLO algorithm, and extract geometric features such as length and angle. Specifically, a recognition method based on 3D laser point cloud images is proposed, where point cloud data are converted into planar images for processing. Experimental results indicate that the laser point cloud detection achieves millimeter-level precision, the distress recall rate exceeds 85%, and the identification precision reaches 79.5%, demonstrating satisfactory detection accuracy and efficiency.
Related Concept Videos
Design Example: Joints in Concrete Pavements
Contraction joints are typically formed by sawing a groove into the concrete shortly after it has hardened. This creates a weakened vertical plane, deliberately encouraging cracking at...
Topographic Surveying and Contours
Design Example: Alignment of a Road Line Using GIS
Methods of Obtaining Topography
Masonry Paving
Shape and Texture of Coarse Aggregate