Aiming at the problems of low accuracy and complex parameter setting in the traffic sign segmentation of a pulse-coupled neural network, we propose an improved pulse-coupled neural network with adjustable parameters (PA-MSPCNN) in this paper. By analyzing the color characteristics of traffic signs, the PA-MSPCNN preprocesses the image with reddening and distinguishes traffic signs and the environmental background. The influence of neighboring neurons on central neurons improves the weighing matrix and the connection coefficient of the MSPCNN. We analyze the relationship between the dynamic thresholds and adjust these more reasonably by adding an auxiliary parameter. The experimental results show that the segmentation accuracy of the PA-MSPCNN on traffic sign images is 85%. The PA-MSPCNN not only reduces the number of parameters in the traditional PCNN model but also accurately segments the image, which has better applicability for complex situations such as changes in illumination conditions, scale changes, and geometric rotation of traffic signs.