An improved hybrid solar cell defect detection approach using Generative Adversarial Networks and weighted classification

Quality control has a vital role in manufacturing processes. Electroluminescence (EL) imaging is one of the main non-destructive inspection methods for quality assessment in the Photovoltaic (PV) module production industry. EL test reveals PV cell defects such as micro cracks, broken cells, finger interruptions and provides detailed information about production quality. In recent years, automated detection and classification systems using deep neural networks for PV module inspection have gained increasing attention. However, deep learning-based systems usually require large amount of labeled data and high computational power. In this work, we proposed a compact classification framework based on hybrid data augmentation and deep learning models for detection of the defective solar cells. In the proposed method, the limited and imbalanced EL datasets were augmented through various Generative Adversarial Networks (GAN), and defect detection was achieved by customized pre-trained Convolutional Neural Networks (CNN). A novel hybrid EL dataset for training was formed by combining public ELPV dataset, our custom real-world dataset and synthetic images created with different GAN architectures such as GAN, cGAN and WGAN-GP. The datasets were classified through proposed customized VGG-16 and other state-of-the-art CNN models. The best results obtained by the proposed CNN model with WGAN-GP augmented dataset are 94.11% mean accuracy in the ELPV test dataset and 93.08% mean accuracy in the custom real-world test dataset. Therefore, the proposed detection system achieved superior performance with lesser resource usage and limited data.