In order to solve the problem of difficulty in target detection caused by noise interference, illumination fluctuation and complex background in UAV aerial infrared images, an infrared target detection model for UAV based on YOLOv8 was proposed. Firstly, the SCDown module in YOLOv10 was introduced to maximize the preservation of contextual semantic information for each scale. Secondly, the dynamic upsampler DySample was introduced to improve the sensitivity of the model to image details. At the same time, the triplet attention mechanism is introduced to improve C2f to strengthen the model′s understanding of the relationship between spatial and channel dimensions and the processing ability of complex data. Finally, a lightweight decoupling head Efficient_Head module is designed to ensure the detection accuracy and greatly reduce the model parameters. Experimental results show that the improved algorithm mAP50 reaches 83.7%, which is 4.2% higher than YOLOv8n, the accuracy is increased by 1.2%, the recall rate is increased by 3.8%, the number of floating point operations isreduced by 2.5%, and the FPS reaches the detection speed of 323.17 fps, which fully shows that the overall performance of the improved algorithm is better than that of other mainstream algorithms, and it can better complete the task of UAV infrared target detection.