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.