A hierarchical framework for three‐dimensional pavement crack detection on point clouds with multi‐scale abnormal region filtering and multimodal interaction fusion

Early crack detection enables timely maintenance actions, which in turn help extend pavement life and reduce maintenance costs. Traditional 2D detection lacks detail, while 3D detection faces accuracy and efficiency challenges. This paper proposes a hierarchical crack detection framework—F2CrackDet-PCD (crack detection based on point cloud data with filtering and fusion). The framework adopts a pre-filtering and fine segmentation strategy (multi-scale anomaly region filtering [MARF]). First, the MARF uses point cloud characteristics to quickly identify potential crack regions. Then, an orthogonal projection converts 3D data into RGB, depth, and normal images, which are combined by MIF-CrackNet (multimodal interaction fusion) to enhance detection accuracy and robustness. Two datasets were developed: RoadScan-2228, capturing realistic road scenes, and CrackNet-1187, emphasizing densely cracked pavement. Experimental results show that the MARF achieves a recall of about 98% on both datasets. F2CrackDet-PCD achieves F1-scores of 75.0 on RoadScan-2228 and 78.2 on CrackNet-1187. F2CrackDet-PCD provides a solution of lane-level 3D point cloud crack detection for large-scale road detection.