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Target recognition and tracking based on multi-rotor UAVs has the advantages of low cost and high flexibility. It can carry out high-intensity monitoring of low-ground and low-altitude targets. It has a huge application prospect in the national defense and military fields and civilian fields. Embedded devices with low power consumption, light weight and high reliability are often used in UAV airborne computers. The computing power of such devices is limited, and the existing target recognition algorithms based on deep learning are difficult to run on such devices in real time. Therefore, it is of great significance to research on the technology of aerial small target recognition based on deep learning and running real time in embedded equipment. A lightweight YOLOv4 backbone network which suitable for overlooking small targets is improved and designed, and the network is pruned based on the γ coefficient of the BN layer, and accelerated using TensorRT. Besides, an image dataset of military target is produced, and it is used for testing the improved algorithm on the airborne embedded computing platform. Compared with the original YOLOv4, the accuracy of the improved algorithm is increased by 2.3% and the speed is increased by 3.3 times.