Benchmarking of Morphological and Textural Descriptors for Automated Thermal Anomaly Detection in Photovoltaic Panels

Automated thermal inspection supports scalable photovoltaic asset management by reducing the subjectivity and limited temporal coverage of manual surveys. This study benchmarks a lightweight machine vision framework for low-resolution infrared inspection of photovoltaic modules using native 24×40 pixel thermal images. Morphological and textural descriptors, namely HOG, LBP, and GLCM, were evaluated with optimized SVM, Random Forest, and XGBoost classifiers under a unified experimental protocol. The HOG + SVMOpt configuration achieved the best performance, with a Macro F1-score of 0.80±0.02 and an average accuracy of 0.80±0.02. The same pipeline maintained an end-to-end CPU latency of 12.45±0.85 ms per image, including preprocessing, descriptor extraction, and prediction. The results indicate that gradient-based structural descriptors provide the most favorable balance between predictive performance and computational cost among the evaluated configurations. The proposed pipeline is therefore presented as an interpretable reference for first-stage thermal screening in low-cost photovoltaic inspection workflows.