Accurate identification of power equipment is a prerequisite for intelligent diagnosis of infrared images.Aiming at the problems of large number of infrared images of power equipment and heavy recognition tasks

intelligent recognition of infrared image of power equipment is realized by transfer learning and region-based fully convolutional networks（R-FCN） algorithm.Firstly

the infrared image dataset of power equipment is made by Labelimg software.Then

based on the principle of transfer learning

the identification model R-FCN which performs excellent on VOC dataset is selected.Finally

the R-FCN detection model is combined with two different convolutional neural network models separately to improve the algorithm

using online example mining（OHEM）method at the same time.The experimental results show that under the same sample conditions

the algorithm can quickly and effectively identify the infrared image of power equipment

and the precision reaches 0.914 3

which has better performance on accuracy and robustness.The algorithm provides a basis for thermal fault diagnosis of power equipment based on infrared images.