随着各大电力公司对无人机（unmanned aerial vehicle，UAV）巡检的大力推广，“机巡为主，人巡为辅”已成为我国电力巡检的主要运维模式。电力线检测作为电力巡检的关键技术，在无人机自主导航、低空避障飞行以及输电线路安全稳定运行等方面发挥着重要作用。众多研究者将输电线路的无人机航拍图像用于线路设备识别与故障诊断，利用机器视觉的方法在电力线检测技术研究中占据主导地位，也是未来的主要发展方向。本文综述了近10年来无人机航拍图像中电力线检测方法的研究进展。首先简述了电力线特征，阐明了电力线检测的传统处理方法的一般流程及所面临的挑战；然后重点阐述了使用传统图像处理方法及深度学习方法的电力线检测原理，前者包括基于Hough变换的方法、基于Radon变换的方法、基于LSD （line segment detector）的方法、基于扫描标记的方法及其他检测方法，后者根据深度卷积神经网络（deep convolutional neural network，DCNN）的结构不同分为基于DCNN的分类方法及基于DCNN的语义分割方法，评述各类方法的优缺点并进行分析与比较，与传统图像处理方法相比，深度学习方法能更有效地实现航拍图像中的电力线检测，并指出基于DCNN的语义分割方法在电力线目标智能识别与分析中发挥着重要作用；随后介绍了电力线检测的常用数据集及性能评价指标；最后针对电力线检测方法目前存在的问题，对下一步的研究方向进行展望。

Power grid is recognized as a key infrastructure for national energy security,and its surveillances and consistency of power lines can be used to facilitate nation's capacity building. In recent years,the growth of electricity demand has been increasing intensively,and the distribution of power lines is much more wide-ranged,and its total mileage has also been extended dramatically. Due to the installation of power lines in a complicated natural environment in relevance to such factors like perennial exposure to wind,sun and rain,coupled with snow and ice coverage and other related extreme weather conditions,the loss or damage of power lines-related equipment is inevitable. Regular inspection has often been implemented to ensure the consistent supply of power and its security surveillances and power lines contexts. Existing methods of power lines-relevant inspection are often in the context of such domains of manual,robot,helicopter,remote sensing satellite,and unmanned aerial vehicle(UAV). The UAV inspection can be recognized as the key mode of power lines inspection in China to some extent. However,UAV-based inspection of power lines is still challenged for a large number of aerial images. Its manual detection is labor intensive,and missing detection or misjudgment is produced as well. Simultaneously,the preservation of power lines is also very challenged for UAV inspection. Such power lines detection plays an important role in autonomous navigation for UAV,low altitude obstacle avoidance flight and safe and stable operation of power grid. Therefore,UAV-captured aerial images analysis is used to detect power equipment,and machine vision is concerned for aerial images-based power lines detection,which can be as one of the potential direction for future research development. The literature is reviewed for decadal growth of machine vision based power lines detection using aerial images-captured source(main data source) and artificial intelligence algorithm(main implementation method). First,the geometric features of power lines in aerial images are briefly illustrated. Traditional image processing method based power lines detection is reviewed in terms of power lines detection,including image pre-processing,edge detection,power lines recognition,power lines fitting,and current challenges in power lines detection are listed below,e. g.,image blurred, complex and changeable background,non-significant power line features,weather conditions and other related factors. Second,two sorts of conventional image processing and deep learning method based power lines detection mechanisms are involved in. In detail,traditional image processing methods for power lines detection are divided into such methods relevant to Hough transform,Radon transform,line segment detector(LSD),scan mark,and its contexts. The network structure of the deep convolutional neural network(DCNN) based deep learning methods are divided into its classification and semantic segmentation for power lines detection. The pros and cons of multiple methods are reviewed and analyzed further. Comparative analysis is carried out as follows:Hough and Radon transform based power lines detection are based on global feature extraction methods,which have some challenges to be resolved like high computational cost and large memory resources. The LSD and scan mark based detection methods have its potentials to optimize Hough and Radon transform. The LSD algorithm is preferred for high precision and short running time,but the algorithm is vulnerable to noise. The power lines-related feature extraction is incomplete based on scan mark,and they are prone to be distorted and fractured. In a word,to meet the requirement for automatic detection of power lines,traditional image processing methods mentioned above cannot be used to identify power lines effectively among many straight lines,and different thresholds are required to be set manually for different application scenarios and some threshold parameters need to be validated further. The deep learning methods for power lines detection can be used to learn and extract image features automatically,and end-to-end power lines detection can be realized without manual-based features design and adjustable threshold parameters. The power lines-related classification methods can be used to detect the coverage or non-coverage of power lines in aerial images only. But,the detailed location of power lines is still unclear while the power lines semantic segmentation methods can be used to extract location information of power lines automatically. Compared to the traditional image processing method,deep learning method is more effective in related to aerial images-derived detection of power lines,which is more accurate and faster than the traditional image processing method,and DCNN-based semantic segmentation method is essential for the intelligent recognition and analysis of power lines. Popular dataset and performance evaluation index of power lines detection are introduced as well. Finally,due to the problems of power lines detection methods based on deep learning is existed,future research work is predicted and focused on integrated dataset and dataset quality evaluation index,annotation of small sample dataset,fusion of multiple deep learning models,deep fusion of multiple learning,and fusion of multi-source data. To improve the stability and real-time performance of detection models,the application of machine vision technology can be greatly facilitated in power lines inspection,even for the whole smart grid further.