This study examines a deep learning and feature‐based approach for the purpose of detecting and classifying defective photovoltaic modules using thermal infrared images in a South African setting. The VGG‐16 and MobileNet models are shown to provide good performance for the classification of defects. The scale invariant feature transform (SIFT) descriptor, combined with a random forest classifier, is used to identify defective photovoltaic modules. The implementation of this approach has potential for cost reduction in defect classification over current methods.

中文翻译：

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使用机器学习方法通​​过热红外成像对光伏缺陷进行分类

这项研究考察了一种基于深度学习和基于特征的方法，目的是在南非的环境中使用热红外图像对有缺陷的光伏模块进行检测和分类。显示了VGG-16和MobileNet模型可为缺陷分类提供良好的性能。尺度不变特征变换（SIFT）描述符与随机森林分类器结合在一起，用于识别有缺陷的光伏模块。与现有方法相比，该方法的实现具有降低缺陷分类成本的潜力。