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：当前，光伏电站的快速发展对现场光伏组件的可靠维护与故障诊断提出了更高要求。由于卷积神经网络的有效性，该技术已广泛应用于现有光伏电池自动缺陷检测领域。然而，这类基于CNN的模型通常参数量庞大，对硬件资源要求严苛，难以在实际工业项目中部署。为解决上述问题，我们提出一种基于神经架构搜索与知识蒸馏的新型轻量化高性能模型，用于电致发光图像中光伏电池缺陷的自动检测。为实现高效轻量模型的自动设计，我们首次将神经架构搜索引入光伏电池缺陷分类领域。针对缺陷尺寸不固定的特点，我们设计了适配的网络搜索结构以充分发挥多尺度特性。为提升搜索所得轻量模型的整体性能，我们进一步基于知识蒸馏技术，将现有预训练大规模模型学习到的知识（包括注意力信息、特征信息、逻辑信息及任务导向信息）进行迁移。实验表明，在线数据增强条件下，所提模型在公开的EL图像光伏电池数据集上达到当前最优性能，准确率为91.74%，参数量仅1.85M。提出的轻量化高性能模型可轻松部署至实际工业项目终端设备，同时保持检测精度。