RD-GRF for Automatic Classification of Surface Defects of Lithium-ion Battery Electrodes

Considering that defect classification is an indispensable element in the production process of lithium-ion battery (LIB), a two-step classification method named RD-GRF is proposed in this paper to classify the six types of surface defects in the LIB electrodes. Firstly, based on the change of the gray value in designated area of the electrode surface and the specific ring area obtained according to the defect shape, the data set is divided into bright defects and dark defects. And then Gabor filter is exploited to extract the features of image, and the Random forest (RF) classifier is used for further detailed classification in the two divided categories. Through experimental results, it is found that the rough classification which is the first step in RD-GRF exactly improves the classification accuracy. All in all, in the case of comprehensively considering classification accuracy and speed, RD-GRF achieves the best result compared with the other three famous algorithms.