A Scale-Aware Multidomain DETR for Small Object Detection in UAV Remote Sensing Imagery

Object detection in UAV remote sensing imagery is significantly challenged by small-scale objects, dense distributions, and complex backgrounds. Existing DETR-based models struggle with multi-scale feature extraction, and traditional multi-head self-attention (MHSA) often introduces redundancy and noise when handling high-frequency details. Moreover, current methods lack effective multi-domain feature modeling. To address these issues, this paper proposes a Scale-Aware Multi-Domain DETR (SME-DETR) for small object detection in UAV remote sensing imagery. Firstly, a Scale-Aware Attentional Backbone (SAA-Backbone) is designed, where the Dynamic Scale-Aware module (DSA) adaptively fuses multi-scale features using depthwise separable convolutions and dynamic weighting. In addition, a Directional Channel Attention Module (DCA) further enhances edge and texture representation. Then, a Multi-domain Augmented Pyramid (MDAP) is constructed, integrating a CSP-based Multi-domain Kernel (CSP-MDKernel) to jointly optimize spatial, frequency, and channel domain features. A Spatial-Channel Fusion Convolution (SCFConv) is employed to preserve fine-grained details. Finally, an Efficient Attention-based Intra-scale Feature Interaction module (EfficientAIFI) is proposed, which replaces the complex matrix operations of traditional multi-head self-attention (MHSA) with linear element-wise multiplication. At the same time, it maintains global dependencies through normalized inner products between queries and keys. Additionally, a lightweight Background-Suppression Gate (BSG) is incorporated to mitigate false positives induced by cluttered backgrounds, further enhancing detection robustness. Experiments on the VisDrone-DET, RSOD, and SeaDronesSeeV2 datasets with the core SME-DETR framework demonstrate that SME-DETR achieves mAP@0.5 scores of 53.0% and 97.8% on the VisDrone-DET and RSOD, respectively, improving the RT-DETR-r18 baseline by 6.0% and 3.0%, respectively. Moreover, SME-DETR significantly outperforms most state-of-the-art detectors.