In order to effectively control the false alarm rate in the process of infrared dim small target detection and improve the accuracy of target detection in complex cloud background, A small target detection algorithm based on spatialfrequency domain mapping and false alarm suppression was proposed in this paper. A directional maximum median filter is constructed according to the intensity values in different directions of the infrared central pixel to effectively eliminate noise. And a background suppression filter is formed by using the intensity difference between the center pixel and its neighboring pixels to fully enhance the dim target. Considering the unique attributes of cloud region, a cloud region recognition mechanism was defined to extract spatial mapping by combining with nonlinear filtering. The Butterworth differential lowpass filter is introduced to complete the coarse saliency detection of the denoised image. And the fine saliency detection was completed according to the amplitude information of the coarse saliency detection result. Then, the threshold was calculated by using the coarse and fine saliency detection results, so that the frequency domain mapping of denoised infrared image was obtained by using the adaptive binary segmentation method. The candidate targets in infrared images were extracted by jointing spatial map and frequency domain map. According to the difference of motion characteristics between real moving target and false alarm, these false alarms in candidate targets were suppressed based on improved pipeline filtering to accurately detect the real infrared dim targets. Test results show that this algorithm can accurately identify the real target, which has a better ROC characteristic curve compared with current infrared dim and small target detection technology