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

2025-02-26

AI Summary

To address the challenges of real-time detection and quantitative risk assessment of power lines and adjacent vegetation in UAV-based power line inspections, this paper proposes a real-time monitoring framework for intelligent transmission line巡检. Methodologically, we pioneer the deep integration of YOLOv8 with directional filters to directly regress oriented bounding boxes (OBBs), significantly improving localization accuracy for slender targets. We further design a vegetation encroachment quantification metric and develop a lightweight, custom post-processing algorithm enabling millimeter-level distance estimation and hierarchical risk classification. Experiments on mainstream power line datasets demonstrate that our method achieves a 5.3% improvement in mAP, reduces OBB localization error by 32%, and attains a real-time inference speed of 42 FPS—outperforming existing approaches. To the best of our knowledge, this is the first solution to enable high-accuracy, robust, and quantifiable real-time vegetation encroachment analysis for power line inspection.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Object Detection & Categorization
• Planning, Routing, and Scheduling: Plan Execution and Monitoring

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Detects power lines and vegetation encroachment in real-time.
• Uses YOLOv8 with directional filters for improved accuracy.
• Develops a metric for quantitative vegetation distribution assessment.

Innovation

• YOLOv8 with directional filters for precise localization
• Oriented Bounding Boxes for accurate power line detection
• Post-processing algorithm for vegetation encroachment metric