In order to overcome the problem of vulnerable disturbance of target detection in complex and variable remote sensing situations, this paper suggests SD-Centernet, which is an optional orientation target detection model combining self-Calibrated module and D_Triplet Attention. This new method introduces rotation angle in the network structure, which provides angular information to the detection box. Self-Calibrated module is introduced into the Dlanet feature extraction network to increase the perceptual field of the output features by fusing information from two different spatial scales through an adaptive calibration operation. Meanwhile, D_Triplet Attention is introduced to enhance the focus of image-based local information, which better solves the cross-dimensional interaction problem. 86.25% detection accuracy and 14.9 fps detection speed have been achieved on SD-Centernet in HRSC-2016 Dataset, that effectively improves the multi-directional target detection in remote sensing aerial photography.