IEEE Systems, Man and Cybernetics Magazine - October 2022 - 23

Table 3. The proposed transfer learning and VGG19
method: a comparison between the balanced and
imbalanced augmented CPLID.
Neural
Network
Transferred
VGG-19
Transferred
VGG-19
Number of
Images
17,800
(Imbalanced
augmented)
16,720
(Balanced
augmented)
of majority samples, FP the number of wrong predictions
of minority samples, and FN the number of wrong predictions
of majority samples. To compare the proposed transfer
learning-based methodology with existing CNN
benchmarks, namely, YOLO-v3, YOLO-v4 [59], and CSPDYOLO
[1], the proposed method is examined on the original
CPLID dataset as well.
In the next step, to prove reliability of the proposed
methodology for generating a larger dataset with image
augmentation, we trained the proposed transferred CNN
two times: once with the original CPLID dataset and
another time with 80% of the large, generated dataset.
For testing, the remaining 20% of the generated dataset
is given to the transferred CNN trained with the two different
datasets separately. Table 2 lists the results for
this step. Table 2 shows that with the same testing datasets,
the larger, inclusive dataset generates better results,
as expected.
In the third step, the effect of balancing the dataset
under study is tested. For this purpose, we perform the
image augmentation with an equal portion of broken
insulator images and intact ones. Therefore, although the
dataset becomes larger and more robust to feature variation
of the images, it would be still an imbalanced dataset.
To implement this step, four augmented images are
generated from each image in the CPLID dataset. Hence,
the overall number of images including the original and
augmented ones becomes 17,800, which is close enough
to the number of the unequally augmented large datasets
with 16,720 insulator images. Table 3 shows the results
of this step. For all the generated datasets mentioned in
this section, 80% of the images are used for training and
the remaining 20% are used for testing. As understood
from Table 3, the results for the balanced dataset are
much better than the case that the dataset is imbalanced.
The results of these three steps show that the proposed
method can detect each insulator image category within
almost half of the time consumed by the other methods in
the literature because of the used transfer learning technique.
The fact that the specifications of the utilized computer
in this study are lower than those used computers in
the benchmarks, makes the result improvements in our
99.93
99.2
99.41
99.2
0.007
Accuracy
(%)
93.22
Precision
(%)
92.9
Recall
(%)
92.11
F1
Score (%)
92.5
Test
Time (s)
0.007
proposed method more noticeable.
The comparison parameters show that
the generated dataset outperforms the
other benchmarks, and it is the most
reliable approach up to now.
Conclusion
In this article, a deep learning methodology
was proposed based on
a transfer learning technique to
improve the solution quality for an
insulator image classification problem.
One contribution of this study is
that the original CPLID dataset has
only 248 broken insulator images among 3,808 images,
which puts this dataset in an imbalanced dataset category.
Using a data-augmentation approach with different
portions, a well-balanced dataset with 16,720 images
was produced, which is a more suitable dataset compared
to the original one with only 3,808 images.
In this proposed transfer learning methodology, a
VGG-19 CNN was implemented as a base model for transfer
learning, which was trained using the ImageNet dataset.
In the second step, the weights of VGG-19 layers,
except the two fully connected final layers, were kept
frozen to perform the feature-extraction task. Weights of
the fully connected final layers were updated using the
insulator image dataset for fine-tuning. This transferred
VGG-19 CNN generated better accuracy results compared
to benchmarks.
The results showed that the proposed transfer learning
technique was able to distinguish the intact and broken
insulator images with more than 99.9% accuracy,
and the required time for insulator image classification
in the proposed technique was roughly half of the reported
time required by the existing methods, thereby well
demonstrating the importance of transfer learning in
advancing the field of insulator image classification for
TLs. Using some intelligent optimization methods [60]-
[66] to select the best parameters given a user dataset is
our next work.
About the Authors
Fatemeh Mohammadi Shakiba (fm298@njit.edu) earned
her B.Sc. degree in computer engineering from Iran University
of Science and Technology, Iran, in 2010, and her M.S.
degree in electrical and computer engineering from Southern
Illinois University, Illinois, USA, in 2018. She is currently
working toward her Ph.D. degree in electrical and computer
engineering at the New Jersey Institute of Technology, Newark,
NJ 07172 USA. Her research interests include artificial
neural networks, machine learning, deep learning, data analyzing
and classification, and fault diagnosis. She is a Student
Member of IEEE.
S. Mohsen Azizi (azizi@njit.edu) earned his B.S. degree
from Sharif University of Technology, Tehran, Iran in 2003,
October 2022 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 23

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