CoMT: A Coupled Multi-Task Learning Algorithm for UAV-based Road Damage Detection

UAV-based road damage detection is essential for intelligent infrastructure maintenance, as it achieves continuous monitoring and timely assessment of pavement conditions. Existing approaches commonly formulate this task as a object detection problem, while overlooking a critical structural prior: road damage inherently occurs within road regions. Consequently, object-detection-based predictions often suffer from limited spatial credibility, particularly in complex scenes containing shadows, lane markings, and other damage-like visual patterns. To address these limitations, this paper proposes CoMT, a coupled multi-task learning algorithm that simultaneously performs road damage detection and road region segmentation in an end-to-end manner. Specifically, a coupled multi-task branch is designed to promote cross-task collaboration through bidirectional feature sharing and interaction. In this design, road-region structural cues serve to constrain damage localization, while damage-related semantic features provide complementary contextual information for region segmentation. Moreover, an orthogonal attention module is introduced to strengthen direction-sensitive damage representations by modeling feature consistency and discrepancy along horizontal and vertical orientations. Furthermore, a spatial-distance fusion loss is developed by integrating Complete IoU and Normalized Wasserstein Distance, enabling the joint optimization of spatial overlap and distributional similarity for more robust bounding-box regression. Experimental results demonstrate that CoMT achieves excellent performance, with 72.3% and 82.0% mAP@50 on UAV-PDD2023 and BDD100K datasets, respectively. The proposed framework also achieves real-time inference and competitive segmentation performance, highlighting its effectiveness and practical potential for UAV-based intelligent road infrastructure inspection and maintenance.