Abstract
Sustained operation of solar photovoltaic assets hinges on accurate detection and prioritization of surface faults across vast, geographically distributed modules. While multi modal imaging strategies are popular, they introduce logistical and economic barriers for routine farm level deployment. This work demonstrates that deep learning and classical machine learning may be judiciously combined to achieve robust surface anomaly categorization and severity estimation from planar visible band imagery alone. We introduce TinyViT which is a compact pipeline integrating Transformer based segmentation, spectral-spatial feature engineering, and ensemble regression. The system ingests consumer grade color camera mosaics of PV panels, classifies seven nuanced surface faults, and generates actionable severity grades for maintenance triage. By eliminating reliance on electroluminescence or IR sensors, our method enables affordable, scalable upkeep for resource limited installations, and advances the state of solar health monitoring toward universal field accessibility. Experiments on real public world datasets validate both classification and regression sub modules, achieving accuracy and interpretability competitive with specialized approaches.
Abstract (translated)
太阳能光伏资产的持续运行依赖于对大面积、地理分布广泛的模块表面故障进行准确检测和优先级排序。虽然多模态成像策略很受欢迎，但它们在日常农场级别的部署中引入了物流和经济障碍。这项工作展示了深度学习与经典机器学习可以巧妙结合，仅通过平面可见光带图像就能实现稳健的表面异常分类和严重程度估计。 我们介绍了TinyViT，这是一个紧凑的工作流程，集成了基于Transformer的分割、光谱-空间特征工程以及集成回归。该系统接收消费者级彩色相机拍摄的光伏板马赛克图，对七种细微的表面故障进行分类，并为维护排期生成可操作的严重程度等级。通过不再依赖电致发光或红外传感器，我们的方法使资源有限的安装能够实现负担得起且可扩展的维护，并推动了太阳能健康监测向普遍现场可访问性的进步。 在真实世界公开数据集上的实验验证了分类和回归子模块的有效性，达到了与专用方法相媲美的准确性和解释能力。
URL
https://arxiv.org/abs/2512.00117
