Journal
NEUROCOMPUTING
Volume 443, Issue -, Pages 199-212Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2021.03.007
Keywords
Power grid surveillance; Early warning classification; Deep learning; Image recognition
Categories
Funding
- National Key R&D Program of China [2018YFB1003800, 2018YFB1003805]
- National Natural Science Foundation of China [61572156, 61832004]
- Shenzhen Science and Technology Program [JCYJ20170413105929681]
- State Grid Shaanxi Electrical Power Company [FWZ-ZB-GWSNDL2002-86, 5226KY19004Y]
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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.
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.
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