GAN-Based Augmentation for Improving CNN Performance of Classification of Defective Photovoltaic Module Cells in Electroluminescence Images

Abstract

Electroluminescence (EL) imaging is an effective way for the examining of photovoltaic (PV) modules. Compared with manual analysis, using Convolutional Neural Network (CNN) for classification is much more convenient but it requires a certain amount of annotated training samples which cannot be acquired handily. In this paper, we present a method for augmenting the existing dataset of EL images using Generative Adversarial Networks (GANS) and propose a model called AC-PG GAN aiming at this. Three chosen CNN models are used to examine the effectiveness of the proposed GAN model and have achieved an improvement of the classification accuracy with the augmented dataset after some adjustment and the maximum improvement is up to 14%.