Infrared image intelligent analysis is an effective method for the fault diagnosis of transformer equipment, and its key technology is target device segmentation. In this study, aiming to address the difficulty in overall segmentation of current transformers with complex backgrounds, the DeepLabv3+ neural network based on ResNet50 was applied to train the semantic segmentation model with infrared image of CT. The collected samples were enhanced by the limited contrast adaptive histogram equalization method, and a sample dataset was constructed. The sample dataset was expanded by image distortion, and a semantic segmentation network was built to train the semantic segmentation model to realize the binary classification of current transformer pixels and background pixels. The test results of 420 current transformer infrared images showed that the MIOU of this method is 87.5%, which can accurately divide the current transformer equipment from the test images and lay a foundation for the subsequent intelligent fault diagnosis of current transformers.