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Nowadays, the research of UAV auto-landing is quite hot. Navigation information plays an important role in the process of autonomous landing. And compared with traditional navigation methods, visual navigation can provide more environmental information, which is conducive to improving the landing safety of UAV. The higher the flying height of UAV, the smaller the landing marker captured by airborne camera. In order to improve the ability of UAV to recognize small landing marker, an algorithm based on yolov5s was proposed. Firstly, a prediction head was added to detect smaller targets. Secondly, the Bi-FPN was used to replace the PANet. Finally, EIoU Loss was used to replace CIoU Loss. The improved algorithm was applied to the landing marker detection. The results show that our algorithm has stronger feature extraction ability and higher detection accuracy in small object detection, which proves the superiority of the improved algorithm.