Advanced YOLO-based Real-time Power Line Detection for Vegetation Management

• URL: http://arxiv.org/abs/2503.00044v1
• Date: Wed, 26 Feb 2025 01:21:06 GMT
• Title: Advanced YOLO-based Real-time Power Line Detection for Vegetation Management
• Authors: Shuaiang Rong, Lina He, Salih Furkan Atici, Ahmet Enis Cetin,
• Abstract summary: This paper introduces an intelligent real-time monitoring framework for detecting power lines and adjacent vegetation.<n>It is developed based on the deep-learning Convolutional Neural Network (CNN), You Only Look Once (YOLO)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Power line infrastructure is a key component of the power system, and it is rapidly expanding to meet growing energy demands. Vegetation encroachment is a significant threat to the safe operation of power lines, requiring reliable and timely management to enhance the resilience and reliability of the power network. Integrating smart grid technology, especially Unmanned Aerial Vehicles (UAVs), provides substantial potential to revolutionize the management of extensive power line networks with advanced imaging techniques. However, processing the vast quantity of images captured by UAV patrols remains a significant challenge. This paper introduces an intelligent real-time monitoring framework for detecting power lines and adjacent vegetation. It is developed based on the deep-learning Convolutional Neural Network (CNN), You Only Look Once (YOLO), renowned for its high-speed object detection capabilities. Unlike existing deep learning-based methods, this framework enhances accuracy by integrating YOLOv8 with directional filters. They can extract directional features and textures of power lines and their vicinity, generating Oriented Bounding Boxes (OBB) for more precise localization. Additionally, a post-processing algorithm is developed to create a vegetation encroachment metric for power lines, allowing for a quantitative assessment of the surrounding vegetation distribution. The effectiveness of the proposed framework is demonstrated using a widely used power line dataset.

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