With the emergence of large image sets and the rapid development of computer hardware especially GPU, Convolutional Neural Network (CNN) has become a successful algorithm in the region of artificial intelligence and exhibit remarkable performance in various machine learning tasks. But the computation complexity of CNN is much higher than traditional algorithms, however, the restrict of limited resources on embedded devices become a challenging issue for making efficient embedded computing. In this study, we propose a efficient convolutional neural networks based on embedded devices for electrical equipment inspection, this efficient neural network is evaluated in term of processing speed. The results show that the proposed algorithm can meet the requirement of real-time video processing on embedded devices.