In order to solve the problems of complex background of aircraft target in optical remote sensing images, the imbalance between detection accuracy and detection speed, and the likelihood of miss detection, an SPSSD model fusing different network modules is proposed.Firstly, Resnet50 is adopted to replace the feature extraction network in SSD300 algorithm, and a controllable dilated convolution module is added to expand the features receptive field to obtain more feature information favorable to target detection.Secondly, shallow feature information is obtained by adding FPN and SPP network, and the features of the expanded receptive field are fused with deep feature information.Then, the feature information is sent to the ECANet to obtain more complete and more judicious feature information.Finally, NWPU-RESISC45 dataset is adopted to input 3400 high-resolution remote sensing images into SPSSD model for iterative training, and mAP of the improved algorithm model finally reaches 92.68%, which is improved by 5.18 percentage point in comparison with that of the previous algorithm, and the detection speed reaches 25.1 frames per second.The experimental results show that the proposed method can effectively balance the detection accuracy and detection speed of aircraft targets, which reduces the miss detection rate to a certain extent.