Efficient deep feature extraction and classification for identifying defective photovoltaic module cells in Electroluminescence images

Electroluminescence (EL) imaging has become the standard test procedure for defect detection throughout the production, installation and operation stages of solar modules. Using this test, defects such as micro cracks, broken cells, and finger interruptions on photovoltaic modules could be easily detected and potential power loss issues could be effectively addressed. Although EL test is a very powerful inspection method, interpreting the EL images could be quite challenging due to the inhomogeneous background and complex defect patterns. Therefore, evaluating the damaged cells and determining the defect severity require expertise, and could be time consuming to apply these processes manually for each cell. Hence, the automatic visual inspection of photovoltaic cells is very important. In this study, a novel automatic defect detection and classification framework for solar cells is proposed. In the proposed Deep Feature-Based (DFB) method, the image features extracted through deep neural networks are classified with machine learning methods such as support vector machines, K-Nearest Neighborhood, Decision Tree, Random Forest and Naive Bayes. Thus, classical machine learning and deep learning techniques are used together. In order to combine the features taken from different deep network architectures in various combinations, the minimum Redundancy Maximum Relevance (mRMR) algorithm is employed for the feature selection. In this way, the dimensions of the feature vectors are reduced and the classification performance is increased with fewer features. With the determination of the best features extracted from different layers of deep neural networks, state-of-the-art results were obtained for both 4-class and 2-class datasets. Moreover, a Lightweight Convolutional Neural Network (L-CNN) architecture has been proposed and trained from scratch, and the results are compared with previous works. As a result, the highest scores are obtained using DFB method with Support Vector Machines (SVM) and classification scores of 90.57% and 94.52% were obtained for the dataset with 4 - class and 2 - class, respectively. The proposed DFB-SVM models outperformed other studies using the same dataset. The results showed that the proposed framework can detect PV cell defects with high accuracy.