Deep Learning-Based Approach to Automated Monitoring of Defects and Soiling on Solar Panels

Ahmed Hamdi, Hassan N. Noura () and Joseph Azar
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Ahmed Hamdi: FEMTO-ST Institute, University Marie et Louis Pasteur, F-90000 Belfort, France
Hassan N. Noura: FEMTO-ST Institute, University Marie et Louis Pasteur, F-90000 Belfort, France
Joseph Azar: FEMTO-ST Institute, University Marie et Louis Pasteur, F-90000 Belfort, France

Future Internet, 2025, vol. 17, issue 10, 1-20

Abstract: The reliable operation of photovoltaic (PV) systems is often compromised by surface soiling and structural damage, which reduce energy efficiency and complicate large-scale monitoring. To address this challenge, we propose a two-tiered image-classification framework that combines Vision Transformer (ViT) models, lightweight convolutional neural networks (CNNs), and knowledge distillation (KD). In Tier 1, a DINOv2 ViT-Base model is fine-tuned to provide robust high-level categorization of solar-panel images into three classes: Normal, Soiled, and Damaged. In Tier 2, two enhanced EfficientNetB0 models are introduced: (i) a KD-based student model distilled from a DINOv2 ViT-S/14 teacher, which improves accuracy from 96.7% to 98.67% for damage classification and from 90.7% to 92.38% for soiling classification, and (ii) an EfficientNetB0 augmented with Multi-Head Self-Attention (MHSA), which achieves 98.73% accuracy for damage and 93.33% accuracy for soiling. These results demonstrate that integrating transformer-based representations with compact CNN architectures yields a scalable and efficient solution for automated monitoring of the condition of PV systems, offering high accuracy and real-time applicability in inspections on solar farms.

Keywords: solar panels; damage detection; soiling detection; deep learning; vision transformers; Dinov2 (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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