SCL-GCN: Stratified Contrastive Learning Graph Convolution Network for pavement crack detection from mobile LiDAR point clouds

Abstract

Accurate pavement crack detection is important for routine maintenance of pavements and reduction of possible traffic accidents. Most existing rule- or learning-based point-level approaches cannot achieve high detection accuracy and efficiency owing to the disorderly arrangement, scattered intensities, diverse crack structures, large data volumes, and complex annotation of mobile laser scanning (MLS) point clouds. To address these issues, we developed SCL-GCN, a Stratified Contrastive Learning Graph Convolution Network with a novel dual-branch architecture for MLS-point cloud-based pavement crack detection. First, a multi-scale graph representation construction module was designed based on a stratification strategy. This module creates strengthened spaces for the raw pavement point cloud and its downsampled subset, from which adjacency matrices and initial representations are generated. The stratification strategy samples neighbors densely in the raw point clouds and sparsely in the downsampled subset to form the neighborhood for each point, utilizing long-range contexts to increase the effective receptive field while lowing the extra computation. Next, a graph feature contrastive learning module is proposed to take advantage of stratified features. This module supervises the learning process of the two branches to avoid learning bias caused by an imbalanced data distribution, promoting convergence and improving performance. The experimental results show that the developed SCL-GCN model outperforms state-of-the-art methods. With a training/testing ratio of only 1:6 and an overall training time of less than 70 min, the average precision, recall, and F1-score of the SCL-GCN reached 75.7%, 75.1%, and 75.2%, respectively.