With the wide application of infrared thermal imaging detection technology in substation inspection robot and transmission line unmanned aerial vehicle (UAV) detection platform, a large number of infrared images with abnormal hot spots on transmission and transformation equipment need to be manually classified and diagnosed periodically, therefore, intelligent diagnosis of these images by intelligent algorithms are urgently needed. At present, classical machine learning algorithms are difficult to effectively identify the abnormal hot spots on infrared image of transmission and transformation equipment. Based on the artificial intelligence deep learning theory, this paper uses Faster RCNN algorithm based on region recommendation network in depth learning algorithm system to detect, identify and locate the heating fault on such infrared images. Based on the image database of heating fault of power transmission and transformation equipment collected by infrared thermal imager, the data set is manually labeled with bounding frames, and the network shared parameters are constructed through alternate training, and the infrared intelligent detection model of abnormal heating of power transmission and transformation equipment is constructed. The method described here provides a new idea for infrared thermography intelligent detection of transmission and transformation equipment.