A hybrid feature extraction system using DenseNet-169 plus Gabor filters with an SVM classifier reaches 99.17% accuracy on augmented solar panel defect data.
Analyzing the performance and efficiency of machine learning algorithms, such as deep learning, decision trees, or support vector machines, on various datasets and applications
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Towards Automated Solar Panel Integrity: Hybrid Deep Feature Extraction for Advanced Surface Defect Identification
A hybrid feature extraction system using DenseNet-169 plus Gabor filters with an SVM classifier reaches 99.17% accuracy on augmented solar panel defect data.