Facta universitatis - series: Electronics and Energetics 2020 Volume 33, Issue 1, Pages: 133-153
https://doi.org/10.2298/FUEE2001133A
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Wind turbine tower detection using feature descriptors and deep learning
Abedini Fereshteh (Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran)
Bahaghighat Mahdi (Department of Electrical Engineering Raja University Qazvin, Iran)
S’hoyan Misak (Department of Information Security National Polytechnic University of Armenia Yerevan, Armenia)
Wind Turbine Towers (WTTs) are the main structures of wind farms. They are
costly devices that must be thoroughly inspected according to maintenance
plans. Today, existence of machine vision techniques along with unmanned
aerial vehicles (UAVs) enable fast, easy, and intelligent visual inspection
of the structures. Our work is aimed towards developing a vision-based
system to perform Nondestructive tests (NDTs) for wind turbines using UAVs.
In order to navigate the flying machine toward the wind turbine tower and
reliably land on it, the exact position of the wind turbine and its tower
must be detected. We employ several strong computer vision approaches such
as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features
(SURF), Features from Accelerated Segment Test (FAST), Brute-Force, Fast
Library for Approximate Nearest Neighbors (FLANN) to detect the WTT. Then,
in order to increase the reliability of the system, we apply the ResNet,
MobileNet, ShuffleNet, EffNet, and SqueezeNet pre-trained classifiers in order
to verify whether a detected object is indeed a turbine tower or not. This
intelligent monitoring system has auto navigation ability and can be used
for future goals including intelligent fault diagnosis and maintenance
purposes. The simulation results show the accuracy of the proposed model are
89.4% in WTT detection and 97.74% in verification (classification)
problems.
Keywords: machine vision, object detection, vision inspection, Wind turbine, deep learning