CSFPR-RTDETR: Real-Time Small Object Detection Network for UAV Images Based on Cross-Spatial-Frequency Domain and Position Relation

Small object detection in uncrewed aerial vehicle (UAV) images is one of the critical aspects for its widespread application. However, due to limited feature extraction for small objects and complex backgrounds, there remain significant issues of missed detections and false alarms. This article proposes a real-time small object detection network for UAV images based on cross spatial frequency domain and position relation (CSFPR-RTDETR). First, we propose a cross-spatial-frequency domain hybrid (CSFH) feature extraction network, which incorporates frequency-domain processing based on the CSP network to effectively capture global contextual features and enhance the distinction between small objects and backgrounds. Second, we propose a position relation decoder that incorporates the two novel geometric priors: IoU and relative angle. Through rational characterization of spatial correlations, this design significantly strengthens the spatial perception capability of the model, thereby improving the detection performance for densely distributed small objects. Finally, we design an efficient small-object high-frequency hybrid encoder, integrating the P2 detection head and proposing a mixed high-frequency enhancement fusion module (MHE-Fusion) to extract fine-grained high-frequency features of small objects, further boosting detection performance. The experimental results demonstrate that CSFPR-RTDETR achieves superior performance on the VisDrone, AI-TOD, and HIT-UAV datasets, with mAP50 metrics reaching 42.3%, 55.4%, and 83.1%, respectively, which is better than other SOTA models. Compared to RT-DETR, CSFPR-RTDETR reduces the parameters of the network by 29.1% while significantly enhancing detection performance: the mAP50 metrics reach notable improvements of 4.6%, 4.4%, and 1.5% on the three datasets, respectively. The source code is available at https://github.com/HuLei-JXNU/CSFPR-RTDETR