Abstract: This paper proposes a robust small object detection method based on multi-scale feature fusion using remote sensing images. When the natural image-based pre-training model is directly applied to the remote sensing images, the large number of parameters and excessive down sampling in widely feature extractions may lead to the disappearances of small objects due to feature gaps. Therefore, based on the distribution of all object sizes in the dataset (i.e., prior knowledge), a lightweight feature extraction module is first integrated via dynamic selection mechanism that allows each neuron to adaptively allocate the receptive field size for detection. Meanwhile, the information reflected by various scale features has different amounts and emphasis. To increase the accuracy of image feature expression, the FPN (feature pyramid networks) module based on adaptive feature weighted fusion is applied by using the grouping convolution to group all feature channels without affecting each other. In addition, deep learning needs a large amount of data to drive. Due to the lack of remote sensing small object dataset, we built a remote sensing plane small object dataset, and processed the plane and small-vehicle objects in DOTA dataset to make its distribution of size meet the requirement of small object detection. Experimental results show that compared with most mainstream detection methods, the proposed method achieves better results on DOTA and self-built datasets.