To solve the problems of low detection accuracy caused by small target size, drastic scale changes, target aggregation, and complex backgrounds in remote sensing image target detection tasks, an enhanced algorithm, DF-YOLOv7, based on YOLOv7, was put forward. This algorithm first enhances the information loss strategy caused by excessive downsampling in YOLOv7, improves the detection accuracy of small objects by modifying the layer structure, and lightens the network model. Secondly, the MRELAN module with multi-receptive fields is proposed to replace part of the ELAN to obtain a more robust multi-scale feature representation and to embed the efficient multi-scale attention mechanism, for cross-spatial learning to adapt to complex scenes. Finally, a contextual dynamic feature refinement module was presented, and the redundant features were filtered to highlight the feature differences of low-level small target information and improve the ability to express dense targets. Compared with YOLOv7, the accuracy of the improved algorithm is increased by 3.3 percentage points and 2.3 percentage points, respectively, and the number of parameters is reduced by 50.8%. Compared to YOLOv5s, it achieves 20.1 percentage points higher on VisDrone.