UAV-Based Powerline Problem Inspection and Classification using Machine Learning Approaches

Publication Date

1-1-2024

Document Type

Conference Proceeding

Publication Title

Proceedings - IEEE 10th International Conference on Big Data Computing Service and Applications, BigDataService 2024

DOI

10.1109/BigDataService62917.2024.00014

First Page

52

Last Page

59

Abstract

As per the statistics provided by the U.S. Depart-ment of Energy, the transmission system of the United States com-prises roughly 160,000 miles of high-voltage transmission lines. Regular maintenance and monitoring of power line damages are essential. The power transmission lines and tower market size value are set to grow at a 4% CAGR between 2021 and 2028. A defective electrical line that goes undetected could cause wildfires, fatalities, and other lethal devastation. The traditional manual inspection method may cause a delay in response to anomalies as it may not be efficient in covering vast areas and is expensive. This made it necessary to establish a system that works effectively with minimal human intervention, cost, and time. In this paper, we propose a machine learning-based approach to eliminate the need for manual inspection using UAVs. The use of UAVs helps prevent any human error, and due to their GPS functionality, the UAV enables precise tracking of power line faults. We used deep learning techniques and models such as YOLOv8 to train the system for powerline fault detection and classification, achieving an average accuracy of up to 84%. A streamlit user interface has also been developed for this system, where users can upload the captured data. Using this input, the trained machine learning model will carry out the subsequent tasks, such as detecting the power line components (transmission tower, conductor, or insulator plate), identifying the faults by further classifying the faults into their sub-classes (broken wires, missing or broken insulator plates, vegetation on power lines, etc.), and reporting the details of the detected anomalies on the user interface.

Keywords

Anomaly Classification, Computer Vision, Deep Learning, Machine Learning, Object Detection, YOLO

Department

Psychology

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