Crack-U $^2$ Net: Multiscale Feature Learning Network for Pavement Crack Detection From Large-Scale MLS Point Clouds

Deep learning-based algorithms detect pavement cracks in an end-to-end manner from Mobile Laser Scanning (MLS) point clouds, achieving impressive results. However, the accuracy of existing methods still has room to improve due to the difficulty of effectively encoding multiscale features and the limited training data. In this paper, we propose a novel pavement crack detection framework, Crack-U
$^2$
Net, which innovatively incorporates a two-level nested U-Net architecture for feature learning. This design enables the learning of intra-stage multiscale features without introducing significant memory and computation costs, resulting in substantial improvements in accuracy. Moreover, to solve the challenge of insufficient training data, we propose a Geometry-based Data Augmentation (GDA) strategy, aiming to expand the pavement dataset while preserving the pavement geometry. Extensive experiments on the Qinghai-Tibet Highway point cloud dataset demonstrate the higher accuracy and efficiency of Crack-U
$^2$
Net over the state-of-the-art methods, achieving an average precision, recall, F1
$\mbox{-}$
score, and accuracy of 83.8%, 77.6%, 80.1%, and 95.8%, respectively.