Pavement breakage detection from unmanned aerial vehicle viewpoint based on improved Detr

Road damage detection is a key step in maintaining road safety and extending road life, and traditional detection methods depend on manual in spection, which is both labor-intensive and lacks efficiency, with limited accuracy. With the development of UAV technology, UAV inspection has become an efficient and low-cost means of road inspection. In this study, we introduce a UAV-based road breakage detection method leveraging an enhanced Detr model. By adding feature pyramid network (FPN) after the CNN feature extraction module and adopting the ViT encoder structure, the model's capability to detect targets of various sizes and identify them in complex backgrounds is significantly improved. The experiments utilized the UAV-PDD2023 dataset, comprising 2,440 road pavement images captured through UAV inspections and annotated with six common types of road damage. The experimental results demonstrate that the enhanced Detr model can efficiently and accurately identify six types of road damage, including longitudinal cracks, transverse cracks, alligator cracks, diagonal cracks, repairs, and potholes. In particular, the detection performance is significantly improved for damage targets in small scales and complex backgrounds. In summary, the UAV road breakage detection method based on enhanced Detr introduced in this study provides effective technical support for UAV road inspection and has important practical application value.