The widespread popularity of unmanned aerial vehicles enables an immense amount of power line inspection data to be collected. It is an urgent issue to employ massive data especially the visible images to maintain the reliability, safety, and sustainability of power transmission. To date, substantial works have been conducted on the data analysis for power line inspection. With the aim of providing a comprehensive overview for researchers interested in developing a deep-learning-based analysis system for power line inspection data, this paper conducts a thorough review of the current literature and identifies the challenges for future study. Following the typical procedure of data analysis in power line inspection, current works in this area are categorized into component detection and fault diagnosis. For each aspect, the techniques and methodologies adopted in the literature are summarized. Valuable information is also included such as data description and method performance. In particular, an in-depth discussion of existing deep-learning-based analysis methods of power line inspection data is proposed. To conclude the paper, several study trends for the future in this area are presented including data quality problems, small object detection, embedded application, and evaluation baseline.

无人飞行器的广泛普及使得可以收集大量的电力线检查数据。利用海量数据（尤其是可见图像）来保持动力传输的可靠性，安全性和可持续性是迫在眉睫的问题。迄今为止，已经进行了用于电力线检查的数据分析的大量工作。为了向有兴趣开发基于深度学习的电力线检查数据分析系统的研究人员提供全面概述，本文对当前文献进行了全面回顾，并确定了未来研究的挑战。按照电力线检查中典型的数据分析程序，该领域的当前工作可分为组件检测和故障诊断。对于每个方面，总结了文献中采用的技术和方法。还包括有价值的信息，例如数据描述和方法性能。特别是，对现有的基于深度学习的电力线检查数据分析方法进行了深入讨论。总结本文，提出了该领域未来的一些研究趋势，包括数据质量问题，小物体检测，嵌入式应用程序和评估基准。