Aiming at the problem of low detection accuracy and detection recall rate of traditional remote sensing image aircraft target detection algorithm in complex background, a remote sensing image aircraft target detection algorithm based on YOLOv4-tiny in deep learning is proposed. First, according to the network structure of YOLOv3 and YOLOv4, the network structure of YOLOv4-tiny is improved, and the CSP feature extraction network in the original algorithm is strengthened to increase its feature extraction ability. Then, use the Mish activation function to replace the original activation function Leaky ReLU to obtain better generalization. Finally, a spatial pyramid pooling module is added to alleviate the sensitivity of the network to the target scale. The experimental results show that: in the conventional high-quality, over-exposed tarmac, boarding gate interference and foggy remote sensing image test, the improved algorithm has excellent detection results, and the final statistical detection accuracy is 98.49%. Compared with the original algorithm, an increase of 1.79%, a recall rate of 97.19%, an increase of 23.2%, and a speed of 8.77ms. The detection effect has been significantly improved and can meet real-time requirements.