Joint-Embedding Predictive Architecture for Solar PV Panel Fault Classification
2026-07-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors studied how to better identify problems in solar panels using thermal infrared images. They created a new method called JEFFNet that combines two types of machine learning techniques to detect faults more accurately and efficiently. JEFFNet was tested on two datasets and performed well at classifying different types of faults as well as simply telling if a panel is healthy or not. It also uses fewer computational resources compared to a previous method. This approach shows promise for automatically monitoring solar panel health using thermal images.
solar photovoltaic (PV)thermal infrared imagingfault classificationself-supervised learningVision TransformerEfficientNetmulticlass classificationbinary classificationmachine learning modelsparameter efficiency
Authors
Seyyedhamid Azimidokht, Mehdi Monemi, Abdelhak Kharbouch, Farid Hamzehaghdam, Mehdi Rasti, Jamshid Aghaei, Emil Kurvinen
Abstract
The rapid expansion of solar photovoltaic (PV) systems has increased the need for reliable and scalable fault classification, as manual inspection is impractical at scale. Thermal infrared (IR) imaging provides a non-contact solution for identifying PV faults; however, accurate classification remains challenging due to class imbalance, limited texture information, and subtle thermal differences. In this work, we investigate the applicability of Joint-Embedding Predictive Architecture (JEPA) for thermal IR PV fault classification across various scenarios and propose JEFFNet (JEPA-EFFicientNet), a multibranch architecture that combines JEPA-based self-supervised representation learning with EfficientNetV2-S-based supervised convolutional feature extraction. JEFFNet fuses semantic representations from a JEPA-pretrained Vision Transformer with convolutional features from EfficientNetV2-S, enabling complementary feature learning. JEFFNet is evaluated on two public thermal IR datasets, PVF-10 and InfraredSolarModules (ISM), for both multiclass and derived binary (healthy/faulty) classification. On PVF-10, JEFFNet achieves an F1-score of $93.21$ and an accuracy of $94.33$ in the 10-class task, and an F1-score of $97.53$ and an accuracy of $96.41$ in the derived 2-class task. On ISM, JEFFNet achieves an F1-score of $72.60$ and an accuracy of $83.88$ in the 12-class task, and an F1-score of $94.69$ and an accuracy of $94.78$ in the derived 2-class task. JEFFNet also uses only 108.6M parameters versus 205.91M for GEPFNet, a 47.2\% reduction. These results demonstrate that combining self-supervised semantic and supervised convolutional features provides an effective, parameter-efficient solution for thermal IR PV fault classification. The source code is publicly available at https://github.com/Azimi2kht/JEFFNet