Aiming at the problem of high miss rate due to the drastic change of target scale, complex background, small and dense targets from the perspective of UAV, an improved YOLOv5s real-time target detection model is proposed. Firstly, a novel hybrid attention mechanism is introduced and embedded into the backbone network to enhance the extraction of crucial target information. Secondly, a new dense residual pyramid pooling is created to improve network information fusion capabilities while reducing computational cost. Then, a C3-BoT module based on multi-head self-attention mechanism is designed to effectively capture the global contextual information of UAV images. Finally, a specialized layer for detecting extremely small targets is added to the YOLOv5s network, specifically tailored to mitigate the issue of miss rate of small objects. Experimental results on the VisDrone2019 dataset show that the improved model achieves an mAP0.5 of 40.6%, an improvement of 8.1 percentage points over the YOLOv5s baseline model, demonstrating superior detection performance in UAV aerial image tasks.