Intelligent extraction of road cracks based on vehicle laser point cloud and panoramic sequence images

Abstract

In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs, this study presents an intelligent strategy for extracting road cracks. This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images. The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously. A convolutional neural network is utilized to extract cracks from the panoramic sequence image. The extracted sequence image is then aligned with the laser point cloud, enabling the assignment of RGB information to the vehicle-mounted three dimensional (3D) point cloud and location information to the two dimensional (2D) panoramic image. Additionally, a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud. The three-dimensional data pertaining to the cracks can be acquired. The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks. The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks. This approach exhibits superior accuracy when compared to conventional methods. Moreover, it facilitates rapid and accurate identification and localization of road cracks, thereby playing a crucial role in ensuring road maintenance and traffic safety. Consequently, this technique finds extensive application in the domains of intelligent transportation and urbanization development. The technology exhibits significant promise for use in the domains of intelligent transportation and city development.