Small object detection is a critical and challenging task in UAV applications due to limited pixel information and feature degradation in deep neural networks. To address these issues, this paper proposes a novel object detector, the Multi-Scale and Multi-Receptive Field Network (MS-MRFNet). A Multi-Scale Weighted Feature Pyramid Network (MSWFPN) is designed to enhance feature fusion across scales by adaptively weighting semantic and spatial features. Additionally, a Multi-Channel Feature Aggregation (MCFA) module is developed to aggregate features at the same level, employing reparameterization trick to retain critical information. Finally, Wise-IoU (WIoU) is adopted as the bounding box regression loss, balancing gradient gains from samples of varying quality across scales and feature groups, thereby accelerating model convergence. Experimental results on the VisDrone 2019, TinyPerson, and NWPU VHR-10 datasets demonstrate mAP50 improvements of 8.6%, 8.4%, and 1.7%, respectively, along with reduced parameter usage. Ablation experiments further validate the effectiveness and applicability of the proposed method in UAV-based small object detection scenarios.