A segmentation and detection method for small infrared targets is proposed to improve the accuracy of target detection. Aiming at the characters of an infrared image, an improved background perception algorithm is used to suppress backgrounds for increasing the target detection probability, and the bound set is constructed to limit the gray level histogram into a Region of Interest (ROI) to reduce the interference of background. Then, target detection is achieved through image segmentation using the maximum entropy of a 2D bound histogram. Furthermore, the fast recurring algorithm is applied to the proposed algorithm for accelerating the running speed of the segmentation algorithm. Experiment results show that proposed background suppression algorithm has the better performance in background suppression and can improve the signal to Noise Ratio (SNR) of the image. Moreover, the segmentation algorithm shows a better effectiveness in target segmentation and detection, and its candidate target is separated more accurate with less false alarm points and running speed has improved by 91%. Through post-process for the image, most of the false alarm points are eliminated, which provides powerful guarantee for the subsequent accurate detection.