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Polycrystalline silicon photovoltaic panel detection
Methods such as current–voltage (I–V) curve measurement, thermal infrared imaging and electroluminescence (EL) imaging have been developed to detect these defects [1, 2]. Detailed examination and interpretation of EL images by experts can both be time-consuming and lead to errors. Monocrystalline and polycrystalline solar panel cells were used in the dataset. The model integrates strategies such as downsampling adjustment, feature fusion. . In response to the low defect detection accuracy caused by small defect areas and large differences in defect scales in EL images of photovoltaic cell components, a defect detection algorithm for photovoltaic cell modules based on traditional image processing and deep learning is proposed.Read More Download PDF
