论文标题
在对电力线的视觉检查中的数据分析综述，深入讨论深度学习技术
Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology
论文作者
论文摘要
无人驾驶飞机的广泛流行使得可以收集大量的电源线检查数据。如何使用大量的检查数据，尤其是可见图像来维持电力传输的可靠性，安全性和可持续性是一个紧迫的问题。迄今为止，已经对电源线检查数据的分析进行了实质性工作。为了为有兴趣开发基于深度学习的电源线检查数据的研究人员提供全面的概述，本文对当前文献进行了详尽的审查，并确定了未来研究的挑战。遵循检查数据分析的典型过程，我们将该区域的当前作品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。还包括一些有价值的信息，例如数据描述和方法性能。此外，提出了对电力线检查中现有与学习相关的分析方法的深入讨论。最后，我们以该领域未来的几种研究趋势结束了本文，例如数据质量问题，小对象检测，嵌入式应用和评估基线。
The widespread popularity of unmanned aerial vehicles enables an immense amount of power lines inspection data to be collected. How to employ massive inspection data especially the visible images to maintain the reliability, safety, and sustainability of power transmission is a pressing issue. To date, substantial works have been conducted on the analysis of power lines inspection data. With the aim of providing a comprehensive overview for researchers who are interested in developing a deep-learning-based analysis system for power lines inspection data, this paper conducts a thorough review of the current literature and identifies the challenges for future research. Following the typical procedure of inspection data analysis, we categorize current works in this area into component detection and fault diagnosis. For each aspect, the techniques and methodologies adopted in the literature are summarized. Some valuable information is also included such as data description and method performance. Further, an in-depth discussion of existing deep-learning-related analysis methods in power lines inspection is proposed. Finally, we conclude the paper with several research trends for the future of this area, such as data quality problems, small object detection, embedded application, and evaluation baseline.
