Aiming at the problems in the process of infrared image identification in the remote monitoring system of substation inspection robot,a segmentation and recognition method of infrared image target equipment based on improved region growing method and BP neural network is proposed.The linear relationship between the brightness and the temperature in the infrared image is fitted by the least squares method,and the temperature field is established based on the pixel.The seed point position of the region growing method is determined according to the set temperature range.The Otsu method is used to determine the optimal segmentation threshold,and the gray level similarity threshold is adopted as the segmentation criterion of the region growing method to complete the optimal segmentation of the window target area.The Hu invariant moments of the binary image of the device are considered as the feature vector of the device shape,and its invariant and interclass divisions are verified.The recognition of the electrical equipment is realized by the BP neural network introducing the additional momentum method and the adaptive adjustment learning rate,and the experimental data show that the optimized BP neural network has the characteristics of fast iteration,poor error volatility,and high classification accuracy.