Infrared images with complex background are usually characterized by low signal-to-noise ratio (SNR), insignificant grayscale changes of adjacent pixels, and easy interference by clutter signal and noise, which makes detection of small infrared targets difficult. In order to solve the above problems, an infrared small target detection algorithm based on feature saliency fusion was proposed. Firstly, in the spatial domain, the grayscale difference between the target and its local background was used to calculate the grayscale saliency map, and in the frequency domain, the background suppressed frequency domain saliency map was calculated by combining the spectral residuals. Secondly, the grayscale significance map and frequency domain significance map were normalized and fused with each other by Hadamard product. Finally, the target region was extracted by adaptive threshold segmentation and Unger filter to eliminate small noise points. Experimental results show that the proposed algorithm can improve the image SNR by tens of times, and has a significant effect on background suppression, and has the advantages of high detection rate and low false alarm rate, which is an effective small target detection algorithm.