The detection of small targets poses a significant challenge for infrared search and tracking (IRST) systems, as they must strike a delicate balance between accuracy and speed. In this paper, we propose a detection algorithm based on spatial attention density peaks searching (SADPS) and an adaptive window selection scheme. First, the difference-of-Gaussians (DoG) filter is introduced for preprocessing raw infrared images. Second, the image is processed by SADPS. Third, an adaptive window selection scheme is applied to obtain window templates for the target scale size. Then, the small target feature is used to enhance the target and suppress the background. Finally, the true targets are segmented through a threshold. The experimental results show that compared with the seven state-of-the-art small targets detection baseline algorithms, the proposed method not only has better detection accuracy, but also has reasonable time consumption.