As an important means of on-line detection of insulators, infrared imaging technology has matured in recent years. But the current analysis of infrared images still mostly depends on manual experience, which is lack of intelligence. The traditional methods of faulty insulator segmentation in infrared images requires complex image preprocessing and manual extraction of target features, which leads to the disadvantages of poor generalization capability and low recognition accuracy in complex contexts. Based on the above problems, this paper proposes a method of faulty insulator segmentation in infrared images based on deep learning. Firstly, an experimental platform was set up to obtain infrared images of insulator strings under complex background. In order to ensure the diversity of infrared images, shooting was conducted in a variety of contamination levels, shooting angles, and backgrounds. Meanwhile, insulator pieces with various degrees of deterioration were selected and placed in various positions. Then, a multi-scale approach is built to achieve the three sub-models of fully convolutional network (FCN) architecture, including FCN-32s, FCN-16s, and FCN-8s. The stochastic gradient descent method was applied to the training of this end-to-end model, and finally, the autonomous division of faulty insulators in infrared images is achieved. After training and comparing the three sub-models, the following results are obtained that the FCN-8s model is the best model for faulty insulator segmentation, and the accuracy rate for verifying the test data set is 89.23%. The results show that the intelligent infrared faulty insulator segmentation method described in this paper has a good segmentation effect, and lays a solid foundation for intelligent infrared on-line monitoring and diagnosis of other high-voltage electrical equipment.