Research on insulator fault detection based on deep learning

Lu Liao; Fenghua Jin

doi:10.1117/12.3038547

23 August 2024 Research on insulator fault detection based on deep learning

Lu Liao, Fenghua Jin

Author Affiliations +

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 1325035 (2024) https://doi.org/10.1117/12.3038547
Event: Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Addressing the challenges posed by the complexity of the background in insulator images captured by drones during power system line inspection, the relatively small proportion of insulator faults in the images, and the blurring effects caused by weather and environmental conditions, this paper proposes improvements to the YOLOv5s detection model. Based on the enhanced detection model, a simulation study of insulator fault detection is conducted. By incorporating Dynamic Snake Convolution (DSC) into the network backbone and adding the GAM (Global attention mechanism) attention mechanism at the bottleneck of the network, as well as utilizing EIOU as the loss function calculation method, three approaches are employed to enhance YOLOv5s. Experimental analysis is conducted using Python, and the results demonstrate that the proposed improvements achieve a 2.1% increase in mean Average Precision (mAP) and a 1.1% increase in recall compared to the original YOLOv5s algorithm, while reducing the model parameters by 5.3%. This enhancement scheme ensures both detection accuracy and model size reduction, promising significant prospects for deployment and application in later stages of the model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Lu Liao and Fenghua Jin "Research on insulator fault detection based on deep learning", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 1325035 (23 August 2024); https://doi.org/10.1117/12.3038547

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KEYWORDS

Convolution

Object detection

Performance modeling

Deep learning

Feature extraction

Target detection

Data modeling

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