Aiming at the problems of insufficient background suppression and high computational complexity of existing infrared small target detection methods, which lead to high false alarm rate and slow response speed of infrared reconnaissance and early warning system, a high-dynamic infrared small targets detection method based on double-neighborhood difference amplification was proposed. Firstly, the mean value characteristics of target and neighborhood in real infrared images was analyzed. Then, the difference between the target area and the inner and outer bilayer neighborhood was calculated, so as to improve the local contrast of bright and weak small targets and suppress complex background and noise. Finally, an adaptive threshold segmentation algorithm was used to obtain the target location. Aiming at the problem that signal-to-clutter ratio gain and background suppression factor were difficult to objectively evaluate the performance of target enhancement and background suppression of infrared image sequences, a target trajectory saliency graph evaluation method was proposed to evaluate the target detection performance of infrared image sequences effectively. Experimental results showed that compared with similar detection methods, the signal-to-clutter ratio gain and background suppression factor of this method were increased by 12% and 10%, respectively, and the running time was shortened by about 34 ms. Therefore, this method is an effective and feasible method for high dynamic infrared small targets detection.