DP-DETR: a lightweight transformer-based model for UAV-based road-defect detection

Abstract Unmanned aerial vehicles offer a cost-effective and flexible solution for road-surface monitoring. However, real-time pavement defect detection from drone perspectives remains challenging due to limited onboard resources and the complex appearance of defects. To address this, this paper proposes the drone’s pavement detection transformer (DP-DETR), a real-time defect detection model based on real-time DETR. Specifically, a lightweight cross-stage partial-ShuffleNetV2 backbone is adopted to enhance efficiency. For accurate detection of diverse defect types, a dynamic deformable crack perception network is introduced. Moreover, a reparameterized multi-scale feature-fusion architecture is designed to strengthen multi-scale feature representation. Evaluated on the RDD2022_ChinaDrone dataset, DP-DETR achieves an mAP@50 of 72.3%, while reducing parameters by 40.93% and computation (GFLOPs) by 31.04% compared to the baseline. The model runs at 58.1 frames per second, demonstrating a superior balance between detection accuracy and real-time performance.