The accuracy of existing image-based methods for aerial imaging of flat-view images is limited. In this paper, a dynamic receptive field-based single-stage object detection algorithm is proposed to address this problem. First, the feature pyramid network is constructed by using SE-ResNeXt. This network is used as the backbone network to extract features efficiently. A bottom-up short connection path and a global context upsampling module are proposed to enhance the structural and semantic features of the detection layer. A dynamic receptive field-based detection subnet is designed to dynamically select the receptive field of an appropriate scale for object detection. Experimental evaluation is conducted on a realistic aerial dataset, and the results are compared with those of other related algorithms. The results show that the improved algorithm performs better on the dataset, and the performance score is evidently increased. It also exhibits good detection capability in scene images such as dim light, down view, oblique view, and dense objects.