综述深度学习在电力线智能巡检中的应用与未来方向
Deep Learning in Automated Power Line Inspection: A Review
- 系统梳理电力线图像分析的检测与故障诊断方法
- 总结现有技术在准确率与实用性上的进展
- 适合从事电力巡检智能化的研究者与工程师参考
近年来，电力线路维护正朝着基于计算机视觉的自动化检测转型。大量视频和图像数据的利用已成为保障电力传输可靠性、安全性和可持续性的关键。近期研究显著聚焦于运用深度学习技术提升电力线巡检效率。本文对现有研究进行全面回顾，旨在帮助研究人员和产业界开发更优的深度学习系统以分析电力线数据。传统数据分析流程被深入剖析，当前研究被系统划分为两大领域：部件检测与故障诊断。文中详细总结了各领域的多样化方法与技术，揭示其功能与应用场景。特别关注深度学习在电力线数据中的分析方法，阐释其基本原理与实际应用。此外，文章还展望了未来研究方向，强调边缘-云协同、多模态分析等技术进步的重要性。因此，本文为深入探索电力线深度学习分析的研究者提供了全面资源，阐明了当前知识边界及未来研究潜力。
原文摘要 · Abstract (English)
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.
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