Aiming at the problems of low detection rate, high time consumption and low storage capacity of target detection model deployed on embedded platform when detecting aerial targets, proposes an aerial target detection method based on optimized YOLOv3 algorithm. The number of model parameters is greatly reduced by model pruning. The traditional anchor box clustering algorithm is optimized and improved by using binary K-means. The CIOU loss function is introduced to enhance the effect of bounding box regression. After the model is optimized and accelerated by TensorRT, the detection model is deployed on JetsonTX2 platform. By selecting a large number of aerial images of different types and different environments to make data sets, the experimental results show that the average accuracy of the optimized algorithm can reach 83. 9% when detecting targets in different aerial images, and the detection speed of each image is improved from 2. 8 FPS to 14. 7 FPS, which meets the requirements of accuracy and real-time.