Photovoltaic cell defect classification based on integration of residual-inception network and spatial pyramid pooling in electroluminescence images

Abstract

Electroluminescence (EL) imaging provides high spatial resolution and better identifies micro-defects for inspection of photovoltaic (PV) modules. However, the analysis of EL images could be typically a challenging process due to complex defect patterns and inhomogeneous background structure. In this study, a deep convolutional neural network (CNN) model using residual connections and spatial pyramid pooling (SPP) is proposed for the efficient classification of PV cell defects. The proposed CNN model is built on the Inception-v3 network. In this way, feature maps in inception modules are shared to reuse in deeper layers and the representation ability of features is enriched with the pooling process of the SPP in different sizes. Due to the imbalanced class distribution, offline data augmentation strategies are applied and network performance is further improved. The proposed method is evaluated on a publicly available dataset of 8 classes, of which 7 classes are defective and one class is defect-free images. In the comparative evaluation, while other approaches give accuracy values between 76.49% and 89.17%, this value is increased to 93.59% with the proposed method. The experimental results show that the proposed method exhibits more accurate and robust classification performance compared with other model combinations and CNN models.