With the emerging large image sets and the rapid development of computer hardware, especially GPU, the deployment of Convolutional Neural Network (CNN) model on embedded devices with limited computing resources becomes a challenging problem. Overheating of power equipment can be identified from infrared thermal images. Because of the fading of infrared radiation in the air, the result of infrared temperature measurement is lower than the actual value. In this study, an efficient CNN based on embedded devices is proposed for thermal fault detection of power equipment. The backbone network of SSD algorithm is replaced by MobileNet. At the same time, batch normalization is combined with the previous volume to reduce model parameters, improve reasoning speed, and make it run on a lightweight computing platform. To solve the problem of infrared radiation loss in the air, an infrared temperature correction unit based on BP neural network is proposed. Based on the above innovation, a thermal fault detection system for power equipment is designed. Experiments and field applications show that the proposed method has high accuracy and reasoning speed.