In view of the complex background of infrared image and the difficulty of segmentation,a threshold segmentation method of infrared image based on improved artificial bee colony sine and cosine optimization is proposed.Firstly,the two-dimensional Otsu function is used as the fitness function of the bee colony algorithm.Secondly,the chaotic opposition learning method and differential evolution method are used to improve the initial population and bee search equation.Then,the improved bee colony algorithm is used to optimize the threshold and reduce the search area of the threshold.Finally,the global optimal solution is calculated by the sine and cosine method,which is the optimal threshold for segmentation.The experimental results show that:compared with Otsu method,k-means method,region growing method and watershed method,the average intersection and union ratio of images target region segmentation is 84.13%,and the average accuracy rate is 89.18%,which effectively improves the segmentation accuracy of infrared image.