The accurate segmentation of the heating chip area of the infrared image of the circuit board is a key step in circuit board fault diagnosis.However,it is difficult to accurately segment the target area due to uneven gray scale,multiple target areas and radiated noise.To solve these problems,an improved PCNN infrared image segmentation module is proposed.Firstly,the gradient information of the infrared image is taken as the input signal of the mode.Secondly,according to the principle of maximum likelihood estimation,the adjustment method of dynamic link coefficient is derived.Finally,the edge constraint algorithm is introduced into the pulse generator to prevent the false capture of neighbor neurons and enhance the segmentation of the target region.The experimental results show that the improved model can effectively reduce the influence of background and radiation noise,and the target area of infrared image of circuit board is accurately segmented.Compared with the known Ostu,K-means and traditional PCNN models in visual effect,region consistency and region contrast,the segmentation performance is significantly enhanced.