Lightweight vision architecture with mutual distillation for robust photovoltaic defect detection in complex environments

Abstract

With the rapid growth of solar photovoltaic installations, defect detection in PV power stations has become crucial for ensuring operational safety and economic efficiency, as undetected defects can lead to significant performance degradation and potential hazards. Unmanned Aerial Vehicle (UAV)-based Electroluminescence (EL) imaging offers an efficient solution for large-scale inspection. However, the harsh environmental conditions and complex imaging scenarios pose significant challenges to detection models, while edge computing deployment demands strict resource constraints. This study introduces SCRViT, a lightweight deep learning model that substantially improves detection performance on low-quality EL images through a spatial-channel reconstruction mechanism and a peer network co-learning strategy. Experimental results demonstrate that the proposed method achieves 88.19% detection accuracy on simulated outdoor environment datasets, surpassing state-of-the-art approaches by 4.77% while reducing model parameters by 55.6%. Through multi-dimensional interpretability studies – including Shapley value feature attribution, GradCAM attention pattern analysis, and information-theoretic mechanism analysis – this research systematically elucidates the model’s environmental adaptation mechanisms. This lightweight yet robust solution enables real-time defect detection on edge devices, improving inspection efficiency and reducing operational costs while providing reliable decision support for practical applications in complex outdoor environments.