In order to solve the problems of poor adaptation to infrared image noise,poor accuracy of segmentation results and complicated parameter determination in image segmentation by Pulse Coupled Neural Network (PCNN), an adaptive segmentation algorithm (AGK-PCNN) in combination with multi-scale Gaussian kernel and Particle Swarm Optimization (PSO) is proposed.Firstly, based on the simplified PCNN, global coupling and pulse synchronization of input images are conducted.The weight matrix is designed by using the characteristics of anisotropic Gaussian kernel, so as to effectively suppress infrared noise.The model key parameters of different images are adaptively adjusted to achieve the optimal segmentation effect by using PSO algorithm.Finally, as for visual effect, a comparison with the maximum inter-class variance method,the adaptive Gaussian thresholding segmentation method and SCM segmentation method is conducted, and a quantitative comparison of the segmented images is conducted by using IoU and Dice score, etc.The results show that the segmentation effect is better than that of other methods in terms of both subjective visual effect and objective indexes.