Feature Selector: an effective module for robust high-voltage switchgear detection

Jianrui Chen; Wei Xie; Langwen Zhang; Xiaoyuan Yu; Weisheng Li

doi:10.1117/12.2685486

15 August 2023 Feature Selector: an effective module for robust high-voltage switchgear detection

Jianrui Chen, Wei Xie, Langwen Zhang, Xiaoyuan Yu, Weisheng Li

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127192U (2023) https://doi.org/10.1117/12.2685486
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

This study proposes a feature extraction module named Feature Selector to enhance the generalization performance of neural networks for state detection of High-Voltage Switchgear (HVS) in outdoor environments. This module comprises three autonomous feature extractors, each intended for extracting distinct types of features, and a switch employed for choosing appropriate features for subsequent propagation. Experimental results on testing sets in three different environments demonstrate that the module effectively improves the generalization performance of the YOLOv5-nano (You Only Look Once) network.

Citation Download Citation

Jianrui Chen, Wei Xie, Langwen Zhang, Xiaoyuan Yu, and Weisheng Li "Feature Selector: an effective module for robust high-voltage switchgear detection", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127192U (15 August 2023); https://doi.org/10.1117/12.2685486

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KEYWORDS

Feature extraction

Switches

Neural networks

Data modeling

Convolution

Circuit switching

Performance modeling

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