EWNet: An early warning classification framework for smart grid based on local-to-global perception

Early warning mechanism is crucial for maintaining the security and reliability of the power grid system. It remains to be a difficult task in a smart grid system due to complex environments in practice. In this paper, by considering the lack of vision-based datasets and models for early warning classification, we constructed a large-scale image dataset, namely EWSPG1.0, which contains 12,113 images annotated with five levels of early warnings. Moreover, 104,448 object instances with respect to ten categories of high-risk objects and power gird infrastructure were annotated with labels, bounding boxes and polygon masks. On the other hand, we proposed a local-to-global perception framework for arly warning classification, namely EWNet. Specifically, a local patch responsor is trained by using image patches extracted from the training set according to the labeled bounding box information of objects. The capability of recognizing high-risk objects and power grid infrastructure is transferred by loading the trained local patch responsor with frozen weights. Features are then fed into a feature integration module and a global classification module for early warning classification of an entire image. In order to evaluate the proposed framework, we benchmarked the proposed framework on our constructed dataset with 11 state-ofthe-art deep convolutional neural networks (CNNs)-based classification models. Experimental results exhibit the effectiveness of our proposed method in terms of Top-1 classification accuracy. They also indicate that vision-based early warning classification remains challengeable under power grid surveillance and needs further study in future work. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The paper introduces the construction of a large-scale image dataset EWSPG1.0 for early warning classification and proposes a local-to-global perception framework EWNet. Experimental results show the effectiveness of the proposed method in terms of Top-1 classification accuracy and indicate the challenges of vision-based early warning classification under power grid surveillance.