The rapid development of UAV technology has driven changes in fields such as intelligent transportation and disaster rescue, and the rise of low-altitude economy has further accelerated its application. In this context, UAV object detection technology faces challenges such as large object size difference, high density, and complex background. To this end, we propose a lightweight and efficient detector, HRSNet, whose core consists of three self-designed modules: the Feature Pyramid Shared Convolution Module (FPSCM) to achieve efficient fusion of multi-scale features, the Dynamic Aware Modulation Module (DAMM) to reduce background interference, and the Hierarchical Receptive Field Scale Detection Head (HRSDH) to improve classification and localization accuracy. Experimental results show that HRSNet outperforms the baseline by 2.1% and achieves 41.2% mAP50 while reducing parameters by 0.28% on the VisDrone dataset, while maintaining a good balance between accuracy and efficiency.