AeroPave-SAM3: Weakly Supervised Morphology-Aware Segmentation of Pavement Distress in High-Resolution UAV Imagery

Accurate pixel-level segmentation of pavement distress from unmanned aerial vehicle (UAV) imagery is important for automated road inspection. However, this task remains challenging because pavement defects often exhibit small spatial scales, low contrast, irregular boundaries, and complex topological structures. Moreover, many UAV pavement datasets provide only bounding-box annotations, which limits their direct use for fine-grained segmentation. To address these issues, this study proposes AeroPave-SAM3, a weakly supervised pavement-distress segmentation framework built on a promptable vision foundation model. First, a box–text dual-prompt mechanism converts bounding-box and class annotations into high-confidence pseudo masks through prompt-consistency evaluation and confidence-aware refinement. Second, a class-conditioned morphology-aware adapter is introduced to model the different characteristics of linear, network-like, and regional distress patterns. Boundary, topology, and consistency constraints are further incorporated to improve contour accuracy and structural continuity. Finally, a global-to-local self-prompted refinement strategy performs native-resolution local inference without manual prompts, enabling better recovery of thin cracks and complex boundaries in high-resolution UAV images. Experiments on UAV-PDD2023 using a manually verified pixel-level evaluation benchmark show that AeroPave-SAM3 achieves 84.36% mIoU, 91.38% Dice, 87.03% Boundary F1, and 90.12% clDice. Compared with the SAM3 baseline, these metrics improve by 3.09, 1.71, 4.42, and 4.41 percentage points, respectively. The results demonstrate that AeroPave-SAM3 provides an effective annotation-efficient solution for fine-grained UAV pavement-distress segmentation.