Few-shot Electric Equipment Classification via Mutual Learning of Transfer-learning Model

Zhou, Bojun; Zhang, Xinsong; Zhao, Jiahao; Zhao, Fengshen; Yan, Chunkai; Xu, Yiming; Gu, Juping

doi:10.1109/cieec54735.2022.9846114

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…To the best of our knowledge, this is one of the first datasets specifically designed for the classification of electrical equipment. This dataset is an extension of our previous EEI-40 [29]. It includes substation equipment, distribution station equipment, and common electrical equipment, ranging from large-scale equipment such as heavy-duty transformers to small-scale equipment such as circuit breakers.…”

Section: Eei-100 Datasetmentioning

confidence: 99%

Global and Local Knowledge Distillation Method for Few-Shot Classification of Electrical Equipment

et al. 2023

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With the increasing utilization of intelligent mobile devices for online inspection of electrical equipment in smart grids, the limited computing power and storage capacity of these devices pose challenges for deploying large algorithm models, and it is also difficult to obtain a substantial number of images of electrical equipment in public. In this paper, we propose a novel distillation method that compresses the knowledge of teacher networks into a compact few-shot classification network, employing a global and local knowledge distillation strategy. Central to our method is exploiting the global and local relationships between the features exacted by the backbone of the teacher network and the student network. We compared our method with recent state-of-the-art (SOTA) methods on three public datasets, and we achieved superior performance. Additionally, we contribute a new dataset, namely, EEI-100, which is specifically designed for electrical equipment image classification. We validated our method on this dataset and demonstrated its exceptional prediction accuracy of 94.12% when utilizing only 5-shot images.

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Section: Eei-100 Datasetmentioning

confidence: 99%

Global and Local Knowledge Distillation Method for Few-Shot Classification of Electrical Equipment

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

Global and Local Knowledge Distillation Method for Few-Shot Classification of Electrical Equipment

Zhou¹,

Zhao²,

Yan³

et al. 2023

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With the increasing utilization of intelligent mobile devices for online inspection of electrical equipment in smart grids, the limited computing power and storage capacity of these devices pose challenges for deploying large algorithm models and it’s hard to obtain a substantial number of images of electrical equipment in public. In this paper, we propose a novel distillation method that compresses the knowledge of teacher networks into a compact few-shot classification network, employing a global and local knowledge distillation strategy. Central to our method is exploiting the global and local relationship between the features exacted by the backbone of the teacher network and student network. We compare our method with recent state-of-the-art (SOTA) methods on three public datasets and achieve superior performance. Additionally, we contribute a new dataset, namely EEI-100, which is specifically designed for classification of electrical equipment. We validate our method on this dataset and demonstrate its exceptional prediction accuracy of 94.12% when utilizing only 5-shot images.

show abstract

Few-shot Electric Equipment Classification via Mutual Learning of Transfer-learning Model

Cited by 2 publications

References 6 publications

Global and Local Knowledge Distillation Method for Few-Shot Classification of Electrical Equipment

Global and Local Knowledge Distillation Method for Few-Shot Classification of Electrical Equipment

Global and Local Knowledge Distillation Method for Few-Shot Classification of Electrical Equipment

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