Search and recognition of targets by using unmanned aerial vehicle depend on the speed and accuracy of target detection algorithms. Aiming at the complex network structure of classic target detection algorithms, high computer performance requirements and slow target detection speed, a real-time detection method based on an improved lightweight detection model (Tiny YOLO-V3) is proposed. First, a new network structure is proposed as the backbone network, compressing the maximum number of channels to 128, further reducing the time complexity and space complexity of the model. Secondly, the single detection head combined with context information is used to enhance the detection ability of targets of different sizes, and the detection speed can be improved while the detection accuracy is maintained. Finally, the remote sensing dataset of Wuhan University is used to carry out the experiment. The experimental results show that the improved model has a significant increase in detection speed, while the accuracy has increased by 0.22.