A lightweight network for photovoltaic cell defect detection in
electroluminescence images based on neural architecture search and knowledge
distillation
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.Comment: 12 pages, 7 figure