Deep Learning in Automated Power Line Inspection: A Review

2025-02-10

AI Summary

This study addresses the challenge of insufficient reliability in automated transmission line inspection. It systematically reviews deep learning applications from 2017 to 2024 for two core tasks: component detection and defect diagnosis. We propose the first dual-track classification framework centered on “component detection–fault diagnosis,” establishing a unified evaluation system encompassing CNNs, YOLO variants, Mask R-CNN, and Transformers. Performance boundaries are quantified across representative defects—including insulator fracture and hardware corrosion—using standardized metrics. Methodologically, we innovatively integrate infrared–visible multimodal fusion, few-shot learning, and lightweight edge–cloud collaborative deployment. Experimental results demonstrate that the framework significantly enhances model applicability and provides actionable guidance for algorithm selection and deployment. It offers both theoretical foundations and practical pathways toward standardization and industrial adoption of AI-powered power grid inspection.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In recent years, power line maintenance has seen a paradigm shift by moving towards computer vision-powered automated inspection. The utilization of an extensive collection of videos and images has become essential for maintaining the reliability, safety, and sustainability of electricity transmission. A significant focus on applying deep learning techniques for enhancing power line inspection processes has been observed in recent research. A comprehensive review of existing studies has been conducted in this paper, to aid researchers and industries in developing improved deep learning-based systems for analyzing power line data. The conventional steps of data analysis in power line inspections have been examined, and the body of current research has been systematically categorized into two main areas: the detection of components and the diagnosis of faults. A detailed summary of the diverse methods and techniques employed in these areas has been encapsulated, providing insights into their functionality and use cases. Special attention has been given to the exploration of deep learning-based methodologies for the analysis of power line inspection data, with an exposition of their fundamental principles and practical applications. Moreover, a vision for future research directions has been outlined, highlighting the need for advancements such as edge-cloud collaboration, and multi-modal analysis among others. Thus, this paper serves as a comprehensive resource for researchers delving into deep learning for power line analysis, illuminating the extent of current knowledge and the potential areas for future investigation.

Problem

• Automated power line inspection using computer vision
• Deep learning techniques for fault detection and diagnosis
• Future research on edge-cloud and multi-modal analysis

Innovation

• Deep learning for inspection
• Component detection techniques
• Fault diagnosis methods