An Improved deep learning method-based detection of transmission line insulator defects

Pengpei Gao; Chunhe Song; Shimao Yu; Tingting Wu

doi:10.1117/12.2681046

23 May 2023 An Improved deep learning method-based detection of transmission line insulator defects

Pengpei Gao, Chunhe Song, Shimao Yu, Tingting Wu

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126452P (2023) https://doi.org/10.1117/12.2681046
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

The insulator is a vital component of power equipment that aids in power transmission. Aiming at transmission line faults caused by missing insulator pieces, this paper proposed to divide insulator detection and defect location into two steps. Additionally, we add Squeeze-and-Excitation (SE) module into the Faster R-CNN model at the insulator detection stage, which could enhance insulator detection accuracy under similar model complexity and also improve the detection efficiency of the YOLOv3 model on insulator defect location.

Citation Download Citation

Pengpei Gao, Chunhe Song, Shimao Yu, and Tingting Wu "An Improved deep learning method-based detection of transmission line insulator defects", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126452P (23 May 2023); https://doi.org/10.1117/12.2681046

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KEYWORDS

Object detection

Defect detection

Feature extraction

Target detection

Deep learning

Education and training

Performance modeling

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