Research on grounding grid corrosion classification method based on convolutional neural network

Abstract

Aiming at the problem that the traditional detection methods can not accurately classify the corrosion degree of grounding grids. The corrosion image is taken as the research object, the convolution neural network is used as the algorithm firstly to classify the corrosion degree. Firstly, the corrosion simulation experiment was carried out, and the sample library was established by using the corrosion image collected in different stages. Then, according to the LeNet-5 model, the traditional CNN and improved CNN models were designed for corrosion classification of grounding grid. Simulation experiments were carried out in the preprocessed samples. Finally, the experimental results of Soft-max and SVM classifier are compared and analyzed. The results show: the classification results of the two models were better than those of the original samples, and the classification performance of SVM is better than that of Soft-max. The improved model can improve classification accuracy. This study fills the blank of detecting the corrosion degree of grounding grid by image method, and it is significant to quickly grasp the corrosion degree to avoid faults or accidents.