FBR-DETR: An Efficient End-to-End Network for Real-Time Small-Object Detection in UAV Imagery

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Identifying and localizing small objects in UAV imagery is a highly challenging task. Existing detection models are prone to missed detections, false detections, and inaccurate localization, while improvements in detection accuracy and real-time performance are often accompanied by an increase in the number of model parameters. To address these issues, this paper proposes an efficient end-to-end network for real-time small-object detection in UAV imagery, termed FBR-DETR. Through coordinated design across three stages, namely feature extraction, efficient encoding, and cross-scale fusion, the proposed network achieves higher detection accuracy and real-time performance for small objects with fewer parameters. FBR-DETR incorporates three core innovations. First, to address the tendency of small objects to be overwhelmed by background textures in deep features, we design a frequency-domain inverse-convolution-enhanced feature extraction network (FICE-Net), which introduces frequency-domain inverse convolution into the backbone feature extraction process to enhance the joint perception of global spectral structures and fine-grained spatial features at the source. Second, to reduce the inference overhead introduced by full-precision attention computation in AIFI, we construct a binary attention-based intra-scale feature interaction module (Binary-AIFI), which binarizes the query and key matrices while preserving global contextual representation with controlled additional computational cost. Third, to alleviate the dilution of small-object information during cross-scale fusion, we propose a re-parameterized cross-scale feature fusion module (RCFF), which enhances feature representation during training through a multi-branch re-parameterized structure and is equivalently merged into a single convolution during inference, thereby balancing fusion capability and real-time inference efficiency. On the VisDrone2019-DET and HIT-UAV datasets, the proposed method achieves mAP0.5 values of 51.2% and 83.4%, respectively. Compared with the baseline model, our method improves mAP0.5 by 5.3% and 5.9%, reduces the number of parameters by 30.8%, and improves the average precision for small objects (APs) by 2.4% and 4.9%, respectively.