A Comparative Analysis of VGG16 and VGG19 for Automated Defect Detection in Solar Panels

The growing demand for solar energy necessitates reliable and efficient solar panel systems. However, various defects like cracks, surface imperfections, dust buildup, and hot spots can significantly impact their performance and longevity. Early detection of these faults is crucial for optimal solar panel operation. This study explores the application of deep learning for automated surface defect detection in solar panels. We employ pre-trained VGG models, specifically VGG16 and VGG19, to analyze images of solar panels captured under various conditions. These images encompass both normal panels and those exhibiting defects. The analysis revealed VGG19's superior performance in defect identification compared to VGG16. VGG19 achieved an impressive 80% precision and an F1 score of 89%, indicating its effectiveness in accurately classifying defective panels. While VGG16 also demonstrated strong results with a precision of 79% and an F1 score of 85%, VGG19's deeper architecture likely contributed to its slight edge in accuracy. These findings highlight the capability of the VGG19 model for visual detection of surface defects in solar panels.