To address the problems of small target size, dense distribution, and occlusion caused false detection and missed detection in unmanned aerial vehicle (UAV) aerial images, this study proposes a small target detection algorithm for aerial images which combines reparameterization and multi-level feature fusion. Firstly, the reparameterized convolution module (RCM) is designed by using the idea of reparameterization, and the C2f-RCM module is designed by combining the RCM with the C2f module, which can effectively draw contextual information by enlarging the sensory field and better extract the subtle features in the images. Secondly, to solve the problem of information loss caused by the neck network in the feature fusion part, this study proposes a multi-level feature fusion module (MFFM), which utilizes cross-level information fusion to effectively reduce the missed detection phenomenon in the case of occlusion, so that the network is able to detect large, medium, and small targets with a significant improved accuracy. Finally, an Inner-Shape IoU bounding box regression loss function is proposed to enhance the convergence speed of the model by constructing auxiliary borders and focusing on the shape of the bounding box. Compared with the baseline model, the proposed method improves mAP@0.5, precision, and recall by 5.7%, 5.7%, and 2.4% in VisDrone2019 and 3.7%, 3.9%, and 5.3% in AI-TOD, respectively, which verifies that the proposed method is effective in detecting small targets in aerial images.