The solar cell

as the core component of photovoltaic power generation module

its quality directly relates to security and efficiency of power generation. So the defect detection of solar cells plays a very important significant role. Taking into account the inefficiencies and high costs of manual detection

this paper proposes to use the convolutional neural network with good image classification performance in the deep learning field to automatically identify and classify the cell. Based on the visualization performance of Tensorboard on Tensorflow platform

the structure of the classical Le Net-5 model is improved and the hyper parameters are adjusted.Moreover

the classification results of the improved LeNet-5 model are compared with that of the classical LeNet-5 model and support vector machine. The results show that the classification effect of the improved LeNet-5 model is the optimal.