In recent years, the rapid development of UAV (Unmanned Aerial Vehicle) technology makes UAV ground target detection technology become an important research direction in the field of computer vision. UAV has a wide range of applications in military investigation, traffic control, and other scenarios. Nevertheless, the UAV images have many problems such as low target resolution, scale changes, environmental changes, multi-target interference, and complex background environment. Aiming at the above difficulties, derived from the original SSD target detection algorithm, this study uses a residual network with better characterization ability to replace the basic network and a residual learning to reduce the network training difficulty and improve the target detection accuracy. By introducing a hopping connection mechanism, the redundancy of the extracted features is reduced, and the problem of performance degradation after the increase of the number of layers is solved. The effectiveness of the algorithm is verified through experimental comparison. Aiming at the problem of target repeated detection and small sample missing detection of the original SSD target detection algorithm, this study proposes an aerial target detection algorithm based on feature information fusion. By integrating information with different feature layers, this algorithm effectively makes up for the difference between low-level visual features and high-level semantic features in neural networks. Results show that the algorithm has sound performance in both detection accuracy and real-time performance.