Abstract: The target detection technology based on convolutional neural network has been rapidly developed and applied. Limited by the detection speed, its large-scale deployment and application on embedded platforms is always difficult. Breaking through the model time complexity on the basis of ensuring model accuracy has become the main problem of target detection technology. one. In order to explore the automatic detection method of targets based on microprocessors in the military field, this paper studies the military target detection system in aerial photography scenes based on YOLOv5, DOTA data set, and JetsonTX2. First, the YOLOv5 model training was completed on the PC side based on the DOTA high-resolution aerial scene target detection data set. The accuracy rate of the model was 54.76%, the recall rate was 81.47%, and the mAP@0.5 reached 74.12%; The target detection and analysis of three potential military target scenarios in the seaport can still achieve good detection results in high-resolution aerial photography scenarios, and the inference speed reaches 181.8FPS. Finally, a military target detection system based on JetsonTX2 and UAV is designed to achieve The algorithm is transplanted from the PC side to the microprocessor side, and the model inference is completed on the JetsonTX2, and the inference speed reaches 16.13FPS.