Dual CNN for photovoltaic electroluminescence images microcrack detection

Accurate detection of microcracks in photovoltaic (PV) cells is crucial for ensuring the efficiency and longevity of solar panels. This study proposes a dual convolutional neural network (Dual-CNN) architecture to enhance microcrack detection in electroluminescence (EL) PV images. By integrating shallow feature extraction with deep semantic analysis, the proposed model effectively captures both fine-grained local textures and high-level structural patterns, addressing the limitations of conventional single-stream CNN models that primarily focus on coarse-grained features. Experimental evaluations on an EL image dataset demonstrate that the Dual-CNN approach significantly improves defect localization and classification with an accuracy of 85.33%, a recall of 71.71% and an F1 score of 73.9%, paving the way for more robust and automated PV inspection systems in the solar energy sector.