Addressing the challenge of low detection accuracy due to large differences in target scale and random direction distribution in remote sensing images, this study proposes a remote sensing object detection method based on a sparse mask Transformer. This approach is based on a Transformer network. First, the angle parameter is added to the Transformer network for realizing appropriate rotational characteristics of remote sensing targets. Then, in the feature extraction section, the multi-level feature pyramid is employed as an input to deal with the large variations of the remote sensing image targets' size and enhance the detection impact for targets with various scales, particularly for small targets. Finally, the self-attention module is replaced with a sparse-interpolation attention module, which efficiently reduces the error due to the large computation amount of Transformer network detecting high-resolution images, and accelerates the network convergence speed during the training phase. The detection findings on the large-scale remote sensing dataset DOTA reveal that the proposed method's average detection accuracy is 78.43% and the detection speed is 12.5 frame/s. Compared to the traditional methods, the proposed method's mean average precision (mAP) is improved by 3.07 percentage points, which shows the proposed method's effectiveness.