With the development of intelligent operation and inspection of power grid

massive image data has been generated by condition monitoring of electric equipment

but there is currently no very effective method for intelligent classification and recognition.In order to solve the problems of data accumulation and misjudgment caused by the difficulty of extracting complex features in images and the lack of learning ability of convolutional neural networks

an image recognition method for electric equipment based on GoogLeNet Inception-V3 model is presented.Firstly

introduce the model structure and implementation steps of electric equipment image recognition

then explain the efficiency and accuracy of the model in feature extraction

and finally prove the advantages of the method through experiments.The research results show that the proposed method has an average recognition accuracy of92% for circuit breakers

current transformers

voltage transformers

insulators and arresters

which is better than the shallow CNN

kNN

VGG-16

GoogLeNet Inception-V1 model is 32.5%

24.0%

6.5% and 4.0% respectively

it has the high feasibility and practical value in engineering.