Target recognition and temperature extraction of the typical component of gas insulated switchgear (GIS) are the key to realizing the infrared intelligent detection of equipment heating state. In this paper,an improved YOLOv4 algorithm based on convolutional block attention module (CBAM) is proposed to achieve rapid target detection and hot spot temperature extraction of GIS bus,disconnector and other components. Firstly,the original infrared images are acquired at a substation site,and an infrared dataset containing typical GIS components is constructed by sharpening the images and marking components. Then,the deep separable convolutional network is used to reduce the amount of model parameters,and the CBAM is integrated to optimize the recognition ability of the model,on the basis of which a GIS infrared component target rapid detection algorithm with improved YOLOv4 is constructed. Finally,the gray-scale difference method is used to extract the temperature value of the hot area for the detected typical target components of GIS. The results show that the proposed algorithm can achieve a recognition speed of 31.5 frame per second and an recognition accuracy of 82.3% on the GIS infrared feature dataset,which is significantly better than other target algorithms. The error between the calculated value and the measured value of temperature rise of GIS components is within ±1℃. The algorithm proposed in this paper can be deployed in edge intelligent terminals such as unmanned aerial vehicles and inspection trolleys to achieve refined identification and rapid diagnosis of the temperature rise status of on-site GIS equipment,thus improving the digitalization and intelligence level of health management of GIS.