A rotation remote sensing target detection algorithm based on multi-scale feature extraction is proposed, because high-resolution remote sensing images have large object scale differences, dense small-object arrangements, and strong orientation. In this study, CenterNet was chosen as the benchmark model and redesigned. First, to improve the context information extraction ability, we proposed and applied the receptive field expansion module combined with multi-scale cavity convolution. Second, the extraction ability of the algorithm for multi-scale targets was improved in combination with adaptive feature fusion. Finally, we redesigned the CenterNet detection head and updated the loss function to improve the detection performance of the model for rotating objects. The designed model is named CenterNet for remote sensing images (CenterNet-RS). Experiments were performed on the DOTA dataset, and the mean average precision (mAP) of CenterNet-RS reaches 73.01%, which is 9.45 percentage points higher than the baseline model. Thus, the experimental findings demonstrate that the proposed method can significantly increase the target detection accuracy for remote sensing images.