The operational environment of UAVs poses unique challenges for object detection compared to conventional methods. When UAVs capture remote sensing images from elevated altitudes, objects often appear minuscule and can be easily obscured by complex backgrounds. This increases the likelihood of false positives and missed detections, thereby complicating the detection process. Furthermore, the hardware resources available on UAV platforms are typically highly constrained. To meet deployment requirements, researchers often must compromise some detection accuracy in favor of a more lightweight model. To address these challenges, we propose PS-YOLO, a fast and precise network specifically designed for UAV-based object detection. In the proposed network, we first design a lightweight backbone based on partial convolution. Then, we introduce a more efficient neck network called FasterBIFFPN to replace the original PAFPN, enabling more effective multi-scale feature fusion. Finally, we propose the GSCD head. GSCD employs shared convolutions to enhance the network’s ability to learn common features across objects of different scales and introduces Normalized Gaussian Wasserstein Distance Loss (NWDLoss) to improve detection accuracy. This detection head effectively increases inference speed without significantly increasing parameter counts. The proposed PS-YOLO is validated on the Visdrone2019 dataset, and the results demonstrate that PS-YOLO provides a 2% improvement in precision, 0.5% improvement in recall, 1.3% improvement in mean average precision (mAP), 41.3% reduction in parameter counts, 6.1% reduction in computational cost, and 26.73 FPS improvement in inference speed compared to the benchmark model YOLOv11-s.

The widespread adoption of small unmanned aerial vehicles poses increasing challenges to public safety. Compared with visible-light sensors, infrared imaging offers excellent nighttime observation capabilities and strong robustness against interference, enabling all-weather UAV surveillance. However, detecting small UAVs in infrared imagery remains challenging due to low target contrast and weak texture features. To address these challenges, we propose IUAV-YOLO, a context-aware detection framework built upon YOLOv10. Specifically, inspired by the receptive field mechanism in human vision, the backbone network is re-designed with a multi-branch structure to improve sensitivity to small targets. Additionally, a Pyramid Global Attention Module is incorporated to strengthen target–background associations, while a Spatial Context-Aware Module is developed to integrate spatial contextual cues and enhance target-background discrimination. Extensive experiments demonstrate that, compared with the baseline model, IUAV-YOLO achieves performance gains of 4.3% in AP0.5 and 2.6% in AP0.5–0.95 on the self-built IRSUAV dataset, with a reduction of 0.7M parameters. On the public SIRST-UAVB dataset, IUAV-YOLO attains improvements of 29.7% in AP0.5 and 16.3% in AP0.5–0.95. Compared with other advanced object detection algorithms, IUAV-YOLO demonstrates a superior accuracy-efficiency trade-off, highlighting its potential for practical infrared UAV surveillance applications.