Outdoor Insulator Surface Condition Evaluation using Image Classification

Pernebayeva, Damira; Sadykova, Diana; James, Alex Pappachen; Bagheri, Mehdi

doi:10.1109/ichve.2018.8642233

Cited by 4 publications

(1 citation statement)

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“…The aerial images done for this study is obtained from drones under extreme winter weather conditions typical of the Eurasian region. We extend our previous work in [14] by including further analysis with data‐driven neural network classifiers such as convolutional neural networks, to draw a comparison with traditional machine learning (ML)‐based approaches. The primary objective in this work is to identify and discriminate the presence of the ice, water, and snow formation over the insulator surface, analysing the aerial images of the insulator.…”

Section: Introductionmentioning

confidence: 99%

High voltage outdoor insulator surface condition evaluation using aerial insulator images

Pernebayeva¹,

Irmanova²,

Sadykova³

et al. 2019

High Voltage

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High voltage insulator detection and monitoring via drone-based aerial images is a cost-effective alternative in extreme winter conditions and complex terrains. The authors examine different surface conditions of the outdoor electrical insulator that generally occur under winter condition using image processing techniques and state-of-the-art classification methods. Two different types of classification approaches are compared: one method is based on neural networks (e.g. CNN, InceptionV3, MobileNet, VGG16, and ResNet50) and the other method is based on traditional machine learning classifiers (e.g. Bayes Net, Decision Tree, Lazy, Rules, and Meta classifiers). They are evaluated to discriminate the images of insulator surface exposed to freezing, wet, and snowing conditions. The results indicate that traditional machine learning methods with proper selection of features can show high classification accuracy. The classification of the insulator surfaces will assist in determining the insulator conditions, and take preventive measures for its protection.

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Section: Introductionmentioning

confidence: 99%