Aero-DETR: a frequency-adaptive sparse-interaction network for UAV-assisted aircraft surface defect detection

Abstract Aircraft surface defect inspection is critical for aviation safety but remains challenging because defects in unmanned aerial vehicle (UAV) imagery are often small, low-contrast, and multi-scale. We construct the aircraft UAV defect inspection dataset (AUDID), containing 2,043 images and 5,669 bounding-box instances: 1,628 dents, 323 cracks, and 3,718 rivet abnormalities. Images from UAV field captures and airline maintenance records were annotated in LabelImg by trained annotators and cross-checked by experienced aircraft maintenance engineers. The training, validation, and test sets contain 1,633, 202, and 208 images, respectively. We further develop Aero-DETR (Aerial Detection Transformer), integrating frequency-adaptive feature enhancement, sparse interaction modeling, and multi-scale feature fusion. At a 640 $$\times$$ 640 resolution, Aero-DETR was compared with faster region-based convolutional neural network (Faster R-CNN), Single Shot MultiBox Detector (SSD), detection transformer (DETR), DETR with improved deNoising anchOr boxes (DINO), you only look once (YOLO), and real-time detection transformer (RT-DETR) variants. On aircraft defects version 2 and AUDID, Aero-DETR obtained mean average precision ( $$\textrm{mAP}_{50:95}$$ ) scores of 54.39% and 34.12%, outperforming RT-DETR with a ResNet-18 backbone (RT-DETR-r18) by 2.90 and 1.64 points, respectively, with 16.34% fewer giga floating-point operations (GFLOPs). On AUDID, it achieved the highest $$\textrm{mAP}_{50}$$ and $$\textrm{mAP}_{50:95}$$ and ran at 133.06 frames per second (FPS) on an NVIDIA A100.