Long-Tail Learning for Three-Dimensional Pavement Distress Segmentation Using Point Clouds Reconstructed from a Consumer Camera

Highlights What are the main findings? A workflow encompassing low-cost and readily deployable acquisition, multi-class pavement distress automated segmentation, and geometric information extraction, based on point clouds. An end-to-end network for segmenting distress from pavement point clouds, incorporating a long-tail class imbalance mitigation strategy and a dual-stream feature fusion module. What are the implications of the main findings? Enables scalable, low-cost 3D pavement inspection and monitoring using consumer-grade imaging, reducing reliance on expensive scanning systems and improving deploy ability for routine inspections. Provides engineering-ready 3D distress outputs to support condition assessment, maintenance prioritization, and integration into intelligent pavement management workflows.Highlights What are the main findings? A workflow encompassing low-cost and readily deployable acquisition, multi-class pavement distress automated segmentation, and geometric information extraction, based on point clouds. An end-to-end network for segmenting distress from pavement point clouds, incorporating a long-tail class imbalance mitigation strategy and a dual-stream feature fusion module. What are the implications of the main findings? Enables scalable, low-cost 3D pavement inspection and monitoring using consumer-grade imaging, reducing reliance on expensive scanning systems and improving deploy ability for routine inspections. Provides engineering-ready 3D distress outputs to support condition assessment, maintenance prioritization, and integration into intelligent pavement management workflows.Abstract The application of 3D data in pavement inspection represents an emerging trend. Acquiring and measuring the 3D information of pavement distress enables a more comprehensive assessment of severity, thereby allowing for accurate monitoring and evaluation of the pavement's technical condition. Existing methods face challenges in high-cost pavement scanning and insufficient research on automated 3D distress segmentation. This study employed a consumer-grade action camera for data acquisition and constructed an engineering-aligned 3D point cloud dataset of pavements. Then a long-tail class imbalance mitigation strategy was introduced, integrating adaptive re-sampling with a weighted fusion loss function, effectively balancing minority class representation. The proposed network, named PointPaveSeg, was a dedicated point cloud processing architecture. A dual-stream feature fusion module was designed for the encoder layer, which decoupled geometric and semantic features to improve distress extraction capability. The network incorporated a hierarchical feature propagation structure enhanced by edge reinforcement, global interaction, and residual connections. Experimental results demonstrated that PointPaveSeg achieved an mIoU of 78.45% and an accuracy of 95.43%. In the field evaluation, post-processing and geometric information extraction were performed on the segmented point clouds. The results showed high consistency with manual measurements. Testing confirmed the method's practical applicability in real-world projects, offering a new lightweight alternative for intelligent pavement monitoring and maintenance systems.