The invention discloses a Mask-RCNN-based electric power equipment infrared image segmentation method, and the method comprises the following steps: S1, building a data set of an electric power equipment infrared image, and marking a training set and a test set; S2, constructing a vertical deep learning model; S3, setting initial hyper-parameters and the number of iterations of the model; S4, using the training set marked in the step S1, and inputting the training set into the constructed model for training; s5, evaluating the performance of the model obtained by the training in the step S4 byadopting the test set marked in the step S1 every 2000-3000 iterations; s6, when the number of iterations reaches a set value, stopping training, and screening out a deep learning model with the optimal performance; and S7, inputting the infrared image of the to-be-tested power equipment into the trained optimal deep learning model for processing, and obtaining a segmentation result. According tothe method, the segmentation precision is remarkably improved, the original color information of the target equipment is reserved, the temperature information can be obtained, and a basis is providedfor fault diagnosis.