IEEE Systems, Man and Cybernetics Magazine - October 2022 - 20

randomly such that from each broken insulator image,
nine augmented images, and from each intact insulator
image, three augmented ones are generated with arbitrarily
various features including brightness, angles, zooming
level, and quality.
The augmentation parameters are chosen randomly
using Python's Augmentor and preprocessing libraries. This
library has features such as flip-left-right, black-and-white,
rotate, skew, zoom, sample, and brightness, which can be
added to a pipeline, providing random values to each
feature. This pipeline can generate the required number of
augmented images that we need [50], [53]. Therefore, this
augmented insulator image dataset includes 16,720 images.
Figures 3 and 4 show some examples of these images.
Proposed Method
This study takes advantage of transfer learning techniques
to improve the fault classification results for a TL
insulators' dataset. To perform this approach, a common
deep CNN called VGG-19 is used for the purpose of
(a)
(b)
Figure 3. Images of an intact insulator. (a) An original image from the CPLID [59] and (b) three augmented
images generated from the original image.
(a)
(b)
Figure 4. Images of a faulty (broken) insulator. (a) An original image from the CPLID [59] and (b) nine
augmented images generated from the original image.
20 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE October 2022

IEEE Systems, Man and Cybernetics Magazine - October 2022

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IEEE Systems, Man and Cybernetics Magazine - October 2022 - Cover1
IEEE Systems, Man and Cybernetics Magazine - October 2022 - Cover2
IEEE Systems, Man and Cybernetics Magazine - October 2022 - Contents
IEEE Systems, Man and Cybernetics Magazine - October 2022 - 2
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IEEE Systems, Man and Cybernetics Magazine - October 2022 - Cover3
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