研究目的
To examine the effectiveness of using basic image processing methods on image data of the power lines acquired by an unmanned aerial vehicle (UAV) for the inspection of power transmission lines, aiming to detect faults in a low-cost and robust way.
研究成果
The proposed methodologies successfully detect power lines and faults in varied natural terrains, offering a low-cost and robust solution for power transmission line inspection. Future work will focus on algorithm optimization and capability expansion.
研究不足
The study acknowledges the need for optimization of the algorithm and expansion of its capabilities, such as applying machine learning techniques for improved detection and extending fault detection to include lines with thinning wire structure using thermal imaging.
1:Experimental Design and Method Selection:
The study proposes two methodologies for detecting power lines in video images captured by a UAV, utilizing Hough transform with modifications for enhanced accuracy and efficiency.
2:Sample Selection and Data Sources:
Videos were captured in real-world scenarios at two different locations in Greece, featuring varied natural terrains.
3:List of Experimental Equipment and Materials:
A custom-assembled UAV equipped with a Raspberry Pi 3 Model B with an 8 MP Raspicam, an action camera SJ5000X, and other components for flight control and video transmission.
4:Experimental Procedures and Operational Workflow:
The methodologies involve grayscale conversion, edge detection using Laplacian of Gaussian and Sobel operator, application of Hough transform, and definition of regions of interest (ROIs) around detected lines for fault detection.
5:Data Analysis Methods:
The performance of the proposed algorithm was evaluated based on processing time and accuracy in detecting power lines and faults.
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Ublox Neo-6M
GPS
Ublox
GPS connectivity for the UAV
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Pixhawk PX4
Flight Controller
PX4
Control of the UAV
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KDS AT9
Remote Controller
KDS
Remote control of the UAV
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S500
Frame for quadcopters
Holds the motors and other components of the UAV
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SunnySky
2216 900KV motors
SunnySky
Propulsion of the UAV
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SIMONK
30A Electronic Speed Controllers
SIMONK
Control the speed of the motors
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Li-Po battery
3S 45C 4000mAh
Powers the UAV
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Raspberry Pi 3 Model B
Raspberry Pi
Video recording
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SJ5000X
Action camera
Records video and images from a different point of view
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TS800 AV
5.8GHz 1500mW Transmitter
Transmits video to the FPV screen
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Boscam Galaxy RD2
FPV screen
Boscam
Displays the video transmitted by the UAV
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