High-altitude UAV photography presents several challenges, including blurry images, low image resolution, and small targets, which can cause low detection performance of existing object detection algorithms. Therefore, this study proposes an improved small-object detection algorithm based on the YOLOv5s computer vision model. First, the original convolution in the network framework was replaced with the SPD-Convolution module to eliminate the impact of pooling operations on feature information and to enhance the model’s capability to extract features from low-resolution and small targets. Second, a coordinate attention mechanism was added after the convolution operation to improve model detection accuracy with small targets under image blurring. Third, the nearest-neighbor interpolation in the original network upsampling was replaced with transposed convolution to increase the receptive field range of the neck and reduce detail loss. Finally, the CIoU loss function was replaced with the Alpha-IoU loss function to solve the problem of the slow convergence of gradients during training on small target images. Using the images of Artemisia salina, taken in Hunshandake sandy land in China, as a dataset, the experimental results demonstrated that the proposed algorithm provides significantly improved results (average precision = 80.17%, accuracy = 73.45% and recall rate = 76.97%, i.e., improvements by 14.96%, 6.24%, and 7.21%, respectively, compared with the original model) and also outperforms other detection algorithms. The detection of small objects and blurry images has been significantly improved.