Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectiveness, convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells. However, the parameters of these CNN-based models are very large, which require stringent hardware resources and it is difficult to be applied in actual industrial projects. To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in electroluminescence(EL) images based on neural architecture search and knowledge distillation. To auto-design an effective lightweight model, we introduce neural architecture search to the field of PV cell defect classification for the first time. Since the defect can be any size, we design a proper search structure of network to better exploit the multi-scale characteristic. To improve the overall performance of the searched lightweight model, we further transfer the knowledge learned by the existing pre-trained large-scale model based on knowledge distillation. Different kinds of knowledge are exploited and transferred, including attention information, feature information, logit information and task-oriented information. Experiments have demonstrated that the proposed model achieves the state-of-the-art performance on the public PV cell dataset of EL images under online data augmentation with accuracy of 91.74% and the parameters of 1.85M. The proposed lightweight high-performance model can be easily deployed to the end devices of the actual industrial projects and retain the accuracy.
翻译:目前,光电发电站的迅速发展要求在实地对光电电池模块进行越来越可靠的维护和故障诊断。由于效果,在光电电池的自动缺陷检测中广泛使用了进化神经网络(CNN),然而,这些CNN型模型的参数非常庞大,需要严格的硬件资源,难以应用于实际工业项目。为了解决这些问题,我们提议了一个新型的轻量级高性能模型,用于在神经结构搜索和知识蒸馏的基础上对光电电池模块进行自动缺陷检测。为了自动设计一个有效的轻度精度模型,我们首次将神经结构搜索引入了光电光电池的自动缺陷检测。由于这些模型的缺陷可能具有任何大小,我们设计了一个适当的网络搜索结构,以便更好地利用多级特征。为了改进搜索的轻度模型的总体性能,我们进一步将基于知识蒸馏的现有经过预先训练的大型模型所学到的知识转移。不同的知识种类,包括关注度、特征信息、记录仪表、在线数据定位模型的运行状况。在拟议的光学模型下,可以实现所展示的在线数据定位的透明性能。