An improved single shot multibox detector (SSD) algorithm is proposed aiming at the problems of slow detection speed of the target proposal based remote sensing image target detection method represented by faster regions with convolutional neural network (R-CNN) and the low performance in small target detection by the SSD algorithm. The algorithm can combine the advantages of the existing detection methods based on target proposal and one-stage target detection to improve the target detection performance. Furthermore, the algorithm replaces the original visual geometry group net with a densely connected network as the backbone network and constructs a feature pyramid between the densely connected modules instead of the original multi-scale feature map. A sample data online acquisition system is designed to verify the accuracy and performance of the proposed algorithm. A sample set of aircraft and playground target is collected as the experimental sample. The network structure stability is verified by training the improved SSD algorithm. Consequently, good results can be achieved without the support of transfer learning. Moreover, the training process is not easy to diverge. By comparing the Faster R-CNN algorithm using ResNet101 as the backbone network and the R-FCN (region-based fully convolutional networks) algorithm, we find that the mean average precision (MAP) of the improved SSD algorithm is 9.13% and 8.48% higher than that of the faster R-CNN and R-FCN algorithms in the test set, respectively. The proposed SSD algorithm improves the MAP in the small target detection by 14.46% and 13.92% compared to the faster R-CNN and R-FCN algorithms, respectively. Detecting a single image takes 71.8 ms, which is 45.7 ms and 7.5 ms less than that of the faster R-CNN and R-FCN algorithms, respectively.