The detection of remote sensing images has a wide range of applications in monitoring the natural environment,military,homeland security and so on,while remote sensing images have the disadvantages of complex background,small target area and difficulty in character extraction.In this paper,a remote sensing image detection algorithm based on selective fusion of multi-scale features is proposed.The proposed algorithm uses the improved Resnet50 as the backbone network,replaces the first convolution of the Resnet50 with dynamic convolution,and replaces the convolution in the ConvBlock module with pyramid convolution to improve feature extraction capability.At the same time,in order to avoid missing the underlying information,the proposed effective spatial channel attention mechanism module is added after the dynamic convolution layer.Finally,the different scale features based on context information are selected to fuse and improve the model′s ability to locate the target object.The experimental results show that the algorithm improves the detection accuracy of remote sensing images while ensuring speed,and the mean average precision (mAP) reaches 91.88% and 90.23%,respectively,on the remote sensing image disclosure data set RSOD and NWPUVHR-10,and thedetection speed reaches 33 FPS.