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Automatic defect detection is gaining huge importance in photovoltaic (PV) field due to limited application of manual/visual inspection and rising production quantities of PV modules. This study is conducted for automatic detection of PV module defects in electroluminescence (EL) images. We presented a novel approach using light convolutional neural network architecture for recognizing defects in EL images which achieves state of the art results of 93.02% on solar cell dataset of EL images. It requires less computational power and time. It can work on an ordinary CPU computer while maintaining real time speed. It takes only 8.07 ms for predicting one image. For proposing light architecture, we perform extensive experimentation on series of architectures. Moreover, we evaluate data augmentation operations to deal with data scarcity. Overfitting appears a significant problem; thus, we adopt appropriate strategies to generalize model. The impact of each strategy is presented. In addition, cracking patterns and defects that can appear in EL images are reviewed; which will help to label new images appropriately for predicting specific defect types upon availability of large data. The proposed framework is experimentally applied in lab and can help for automatic defect detection in field and industry. • A framework using CNN is proposed for automatic detection of defects in PV cells. • It achieved state of the art results of 93.02% accuracy on EL image dataset. • It can work on ordinary CPU computer while maintaining real time speed (8.07 ms). • Data augmentation operations are evaluated that increase accuracy up to 6.5%. • Defect types appeared in EL images are discussed that can help for manual labelling. [ABSTRACT FROM AUTHOR]