面向公用事业规模光伏阵列巡检的红外计算机视觉
Infrared Computer Vision for Utility-Scale Photovoltaic Array Inspection
AI总结：
本文针对公用事业规模光伏阵列故障巡检，综述常见故障与检测方法，提出基于红外热成像与计算机视觉的自动化热异常检测方案，并展示了数据采集、基准数据集及Hough变换分割等实测结果。
摘要：
公用事业规模的太阳能阵列需要专门的巡检方法来检测故障组件。光伏（PV）组件因天气、接地漏电、电路问题、温度、环境、老化及其他损坏而产生的故障形式多样，但在症状上通常表现为温度差异。本文包含一项小型综述，回顾这些常见故障及光伏阵列故障检测方法。其中，红外热成像相机是改进现场太阳能组件巡检的有力工具，可与图像处理和机器学习等其他技术结合使用。本立场论文考察了若干种在红外图像中自动检测热异常的计算机视觉算法。我们展示了红外热成像数据采集方法、光伏热成像基准数据集，以及图像处理变换（包括用于光伏分割的Hough变换）的实测性能。本文呈现了该实现的结果，并对未来工作进行了讨论。
英文摘要：
Utility-scale solar arrays require specialized inspection methods for detecting faulty panels. Photovoltaic (PV) panel faults caused by weather, ground leakage, circuit issues, temperature, environment, age, and other damage can take many forms but often symptomatically exhibit temperature differences. Included is a mini survey to review these common faults and PV array fault detection approaches. Among these, infrared thermography cameras are a powerful tool for improving solar panel inspection in the field. These can be combined with other technologies, including image processing and machine learning. This position paper examines several computer vision algorithms that automate thermal anomaly detection in infrared imagery. We demonstrate our infrared thermography data collection approach, the PV thermal imagery benchmark dataset, and the measured performance of image processing transformations, including the Hough Transform for PV segmentation. The results of this implementation are presented with a discussion of future work.
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