用普通相机图像实现太阳能板表面故障分类与严重度评估
TinyViT: Field Deployable Transformer Pipeline for Solar Panel Surface Fault and Severity Screening
- 结合Transformer分割与特征工程，仅靠可见光图像识别故障
- 分类7类故障，严重度预测准确，媲美专业设备方案
- 适合资源有限的光伏电站，部署成本低、可大规模应用
太阳能光伏资产的长期运行依赖于对分布广泛、地理分散的光伏组件表面故障的精准检测与优先级排序。尽管多模态成像策略流行，但其在日常农场级部署中带来物流与经济障碍。本文证明，通过合理结合深度学习与传统机器学习，仅使用平面可见光影像即可实现稳健的表面异常分类与严重度估计。我们提出TinyViT，一个集成Transformer分割、光谱-空间特征工程与集成回归的轻量级管道。系统输入消费级彩色相机拼接图像，可分类七类细微表面故障，并生成用于维护分级的可行动严重度评分。通过摆脱对电致发光或红外传感器的依赖，该方法使资源受限场景下的低成本、可扩展维护成为可能，推动光伏健康监测向普遍野外可用迈进。在真实公开数据集上的实验验证了分类与回归子模块的有效性，性能与专业方法相当，兼具高准确率与可解释性。
原文摘要 · Abstract (English)
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.
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