Aiming at the problems of low target detection accuracy and difficulty in detecting small targets in current UAV infrared aerial photography images, this paper proposes an improved UAV infrared aerial photography target detection method based on YOLOv8. Dual channel feature fusion structure is introduced to increase the feature fusion capability and reduce the loss of feature information. The lightweight small target detection layer is added to improve the detection ability of the model to the infrared small target. The lightweight convolutional module GSConv is used to replace the traditional convolution in the neck network C2f, reducing the size of the model and improving the detection speed of the model. Finally, the convolutional attention module is added to the SPPF module of the backbone network to further increase the model’s attention to infrared target information and improve the accuracy of model detection. The feasibility of the improved network is verified through experiments. Compared with the benchmark model YOLOv8n, the accuracy rate is increased by 4.1%, and the average accuracy of mAP50 is increased by 3.7. Compared with eight current mainstream models, the model proposed in this paper has the highest accuracy, reaching 83.3%, and the FPS reaching 153. The effectiveness of the method is proved.