浏览全部资源
扫码关注微信
Published： 16 October 2023 ，
移动端阅览
刘传洋， 吴一全， 刘景景. 2023. 无人机航拍图像中电力线检测方法研究进展. 中国图象图形学报， 28(10):3025-3048
Liu Chuanyang， Wu Yiquan， Liu Jingjing. 2023. The growth of UAV aerial images-related power lines detection： a literature review of 2023. Journal of Image and Graphics， 28(10):3025-3048
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.
机器视觉电力线检测无人机巡检图像处理深度学习语义分割
machine visionpower lines detectionunmanned aerial vehicle （UAV） inspectionimage processingdeep learningsemantic segmentation
Abdelfattah R， Wang X F and Wang S. 2021. TTPLA： an aerial-image dataset for detection and segmentation of transmission towers and power lines//Proceedings of the 15th Asian Conference on Computer Vision. Kyoto， Japan： Springer： 601-618 ［DOI： 10.1007/978-3-030-69544-6_36http://dx.doi.org/10.1007/978-3-030-69544-6_36］
Badrinarayanan V， Kendall A and Cipolla R. 2017. SegNet： a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence， 39（12）： 2481-2495 ［DOI： 10.1109/TPAMI.2016.2644615http://dx.doi.org/10.1109/TPAMI.2016.2644615］
Baker L， Mills S， Langlotz T and Rathbone C. 2016. Power line detection using Hough transform and line tracing techniques//Proceedings of 2016 International Conference on Image and Vision Computing. Palmerston North， New Zealand： IEEE： 1-6 ［DOI： 10.1109/IVCNZ.2016.7804438http://dx.doi.org/10.1109/IVCNZ.2016.7804438］
Bian J， Hui X L， Zhao X G and Tan M. 2019. A monocular vision-based perception approach for unmanned aerial vehicle close proximity transmission tower inspection. International Journal of Advanced Robotic Systems， 16（1）： #172988141882022 ［DOI： 10.1177/1729881418820227http://dx.doi.org/10.1177/1729881418820227］
Candamo J and Goldgof D. 2008. Wire detection in low-altitude， urban， and low-quality video frames//Proceedings of the 19th International Conference on Pattern Recognition. Tampa， USA： IEEE： 1-4 ［DOI： 10.1109/ICPR.2008.4761566http://dx.doi.org/10.1109/ICPR.2008.4761566］
Candamo J， Goldgof D， Kasturi R and Godavarthy S. 2010. Detecting wires in cluttered urban scenes using a Gaussian model//Proceedings of the 20th International Conference on Pattern Recognition. Istanbul， Turkey： IEEE： 432-435 ［DOI： 10.1109/ICPR.2010.114http://dx.doi.org/10.1109/ICPR.2010.114］
Candamo J， Kasturi R， Goldgof D and Sarkar S. 2009. Detection of thin lines using low-quality video from low-altitude aircraft in urban settings. IEEE Transactions on Aerospace and Electronic Systems， 45（3）： 937-949 ［DOI： 10.1109/TAES.2009.5259175http://dx.doi.org/10.1109/TAES.2009.5259175］
Cao H P， Zeng W M， Shi Y H and Xu P. 2018. Power line detection based on Hough transform and total least squares method. Computer Technology and Development， 28（10）： 164-167
操昊鹏， 曾卫明， 石玉虎， 徐鹏. 2018. 基于Hough变换和总体最小二乘法的电力线检测. 计算机技术与发展， 28（10）： 164-167 ［DOI： 10.3969/j.issn.1673-629X.2018.10.034http://dx.doi.org/10.3969/j.issn.1673-629X.2018.10.034］
Cao W R， Yang X Y， Zhu L L， Han J D and Wang T R. 2013a. Power line detection based on symmetric partial derivative distribution prior//Proceedings of 2013 IEEE International Conference on Information and Automation. Yinchuan， China： IEEE： 767-772 ［DOI： 10.1109/ICInfA.2013.6720397http://dx.doi.org/10.1109/ICInfA.2013.6720397］
Cao W R， Zhu L L， Han J D， Wang T R and Du Y K. 2013b. High voltage transmission line detection for UAV based routing inspection//Proceedings of 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Wollongong， Australia： IEEE： 554-558 ［DOI： 10.1109/AIM.2013.6584150http://dx.doi.org/10.1109/AIM.2013.6584150］
Carson C， Belongie S， Greenspan H and Malik J. 2002. Blobworld： image segmentation using expectation-maximization and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence， 24（8）： 1026-1038 ［DOI： 10.1109/TPAMI.2002.1023800http://dx.doi.org/10.1109/TPAMI.2002.1023800］
Cerón A， Mondragón B I F and Prieto F. 2014. Power line detection using a circle based search with UAV images//Proceedings of 2014 International Conference on Unmanned Aircraft Systems （ICUAS）. Orlando， USA： IEEE： 632-639 ［DOI： 10.1109/ICUAS.2014.6842307http://dx.doi.org/10.1109/ICUAS.2014.6842307］
Chen C， Mai X M， Song S， Peng X Y， Xu W X and Wang K. 2015. Automatic power lines extraction method from airborne LiDAR point cloud. Geomatics and Information Science of Wuhan University， 40（12）： 1600-1605
陈驰， 麦晓明， 宋爽， 彭向阳， 徐文学， 王珂. 2015. 机载激光点云数据中电力线自动提取方法. 武汉大学学报（信息科学版）， 40（12）： 1600-1605 ［DOI： 10.13203/j.whugis20130573http://dx.doi.org/10.13203/j.whugis20130573］
Chen L C， Papandreou G， Kokkinos I， Murphy K and Yuille A L. 2018. DeepLab： semantic image segmentation with deep convolutional nets， atrous convolution， and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence， 40（4）： 834-848 ［DOI： 10.1109/TPAMI.2017.2699184http://dx.doi.org/10.1109/TPAMI.2017.2699184］
Chen M L， Wang Y Z， Dai Y， Yan Y F and Qi D L. 2022. Small and strong： power line segmentation network in real time based on self-supervised learning. Proceedings of the CSEE， 42（4）： 1365-1374
陈梅林， 王逸舟， 戴彦， 闫云凤， 齐冬莲. 2022. SaSnet： 基于自监督学习的电力线实时分割网络. 中国电机工程学报， 42（4）： 1365-1374 ［DOI： 10.13334/j.0258-8013.pcsee.210504http://dx.doi.org/10.13334/j.0258-8013.pcsee.210504］
Chen Y P， Li Y， Zhang H X， Tong L， Cao Y X and Xue Z H. 2016. Automatic power line extraction from high resolution remote sensing imagery based on an improved Radon transform. Pattern Recognition， 49： 174-186 ［DOI： 10.1016/j.patcog.2015.07.004http://dx.doi.org/10.1016/j.patcog.2015.07.004］
Dong H Y and Huang T T. 2015. Transmission line extraction method based on Hough transform//Proceedings of the 27th Chinese Control and Decision Conference （2015 CCDC）. Qingdao， China： IEEE： 4892-4895 ［DOI： 10.1109/CCDC.2015.7162800http://dx.doi.org/10.1109/CCDC.2015.7162800］
Dong J J， Chen W and Xu C. 2019. Transmission line detection using deep convolutional neural network//Proceedings of the 8th Joint International Information Technology and Artificial Intelligence Conference （ITAIC）. Chongqing， China： IEEE： 977-980 ［DOI： 10.1109/ITAIC.2019.8785845http://dx.doi.org/10.1109/ITAIC.2019.8785845］
Fu L and Lu S T. 2011. Obstacle detection algorithms for aviation//Proceedings of 2011 International Conference on Computer Science and Automation Engineering. Shanghai， China： IEEE： 710-714 ［DOI： 10.1109/CSAE.2011.5952944http://dx.doi.org/10.1109/CSAE.2011.5952944］
Gubbi J， Varghese A and Balamuralidhar P. 2017. A new deep learning architecture for detection of long linear infrastructure//Proceedings of the 15th IAPR International Conference on Machine Vision Applications. Nagoya， Japan： IEEE： 207-210 ［DOI： 10.23919/MVA.2017.7986837http://dx.doi.org/10.23919/MVA.2017.7986837］
Han J M， Yang Z， Xu H， Hu G X， Zhang C， Li H C， Lai S X and Zeng H R. 2020. Search like an eagle： a cascaded model for insulator missing faults detection in aerial images. Energies， 13（3）： #713 ［DOI： 10.3390/en13030713http://dx.doi.org/10.3390/en13030713］
Hao Y P， Liu G T， Xue Y W， Zhu J L， Shi Z W and Li L C. 2014. Wavelet image recognition of ice thickness on transmission lines. High Voltage Engineering， 40（2）： 368-373
郝艳捧， 刘国特， 薛艺为， 朱俊霖， 史尊伟， 李立浧. 2014. 输电线路覆冰厚度的小波分析图像识别. 高电压技术， 40（2）： 368-373 ［DOI： 10.13336/j.1003-6520.hve.2014.02.006http://dx.doi.org/10.13336/j.1003-6520.hve.2014.02.006］
Hota M， Rao B S and Kumar U. 2020. Power lines detection and segmentation in multi-spectral UAV images using convolutional neural network//Proceedings of 2020 International Conference on India Geoscience and Remote Sensing Symposium （InGARSS）. Ahmedabad， India： IEEE： 154-157 ［DOI： 10.1109/InGARSS48198.2020.9358967http://dx.doi.org/10.1109/InGARSS48198.2020.9358967］
Huang J T， Gao H L and Dai Z K. 2021. Semantic segmentation method of power line on mobile terminals based on encoder-decoder structure. Journal of Computer Applications， 41（10）： 2952-2958
黄巨挺， 高宏力， 戴志坤. 2021. 基于编码解码结构的移动端电力线语义分割方法. 计算机应用， 41（10）： 2952-2958 ［DOI： 10.11772/j.issn.1001-9081.2020122037http://dx.doi.org/10.11772/j.issn.1001-9081.2020122037］
Huang T T. 2015. Study on Unmanned Aerial Vehicle Automatic Line-Tracking. Shenyang： Shenyang Ligong University
黄婷婷. 2015. 无人机自动巡线方法研究. 沈阳： 沈阳理工大学
Hui X L， Bian J， Zhao X G and Tan M. 2018. Vision-based autonomous navigation approach for unmanned aerial vehicle transmission-line inspection. International Journal of Advanced Robotic Systems， 15（1）： #172988141775282 ［DOI： 10.1177/1729881417752821http://dx.doi.org/10.1177/1729881417752821］
Jiang Z B and Zou K S. 2021. Power line extraction algorithm based on stage attention mechanism. Journal of Data Acquisition and Processing， 36（4）： 812-821
姜振邦， 邹宽胜. 2021. 基于阶段注意力机制的电力线提取算法. 数据采集与处理， 36（4）： 812-821 ［DOI： 10.16337/j.1004-9037.2021.04.019http://dx.doi.org/10.16337/j.1004-9037.2021.04.019］
Lee S J， Yun J P， Choi H， Kwon W， Koo G and Kim S W. 2017. Weakly supervised learning with convolutional neural networks for power line localization//Proceedings of 2017 IEEE Symposium Series on Computational Intelligence （SSCI）. Honolulu， USA： IEEE： 1-8 ［DOI： 10.1109/SSCI.2017.8285410http://dx.doi.org/10.1109/SSCI.2017.8285410］
Li C Y， Yan G J， Xiao Z Q， Li X W， Guo J and Wang J D. 2007. Automatic extraction of power lines from aerial images. Journal of Image and Graphics， 12（6）： 1041-1047
李朝阳， 阎广建， 肖志强， 李小文， 郭军， 王锦地. 2007. 高分辨率航空影像中高压电力线的自动提取. 中国图象图形学报， 12（6）： 1041-1047 ［DOI： 10.3969/j.issn.1006-8961.2007.06.014http://dx.doi.org/10.3969/j.issn.1006-8961.2007.06.014］
Li D D and Wang X Y. 2019. The future application of transmission line automatic monitoring and deep learning technology based on vision//Proceedings of the 4th International Conference on Cloud Computing and Big Data Analysis （ICCCBDA）. Chengdu， China： IEEE： 131-137 ［DOI： 10.1109/ICCCBDA.2019.8725702http://dx.doi.org/10.1109/ICCCBDA.2019.8725702］
Li P Y， Hao X Y， Li J S， Yang J F and Wang G J. 2019. Research on detection algorithm of linear power line in UAV image. Electronic Measurement Technology， 42（19）： 148-153
李朋月， 郝向阳， 李建胜， 杨戬峰， 王高杰. 2019. 无人机影像中直线类电力线检测算法研究. 电子测量技术， 42（19）： 148-153 ［DOI： 10.19651/j.cnki.emt.1903273http://dx.doi.org/10.19651/j.cnki.emt.1903273］
Li S L. 2018. Research on Semantic Segmentation Method of Key Components of Aerial Transmission Line Based on FCN. Beijing： North China Electric Power University
李胜利. 2018. 基于FCN的航拍输电线路关键部件语义分割方法研究. 北京： 华北电力大学
Li Y， Pan C F， Cao X B and Wu D P. 2019. Power line detection by pyramidal patch classification. IEEE Transactions on Emerging Topics in Computational Intelligence， 3（6）： 416-426 ［DOI： 10.1109/TETCI.2018.2849414http://dx.doi.org/10.1109/TETCI.2018.2849414］
Li Z R， Liu Y E， Walker R， Hayward R and Zhang J L. 2010. Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform. Machine Vision and Applications， 21（5）： 677-686 ［DOI： 10.1007/s00138-009-0206-yhttp://dx.doi.org/10.1007/s00138-009-0206-y］
Liu K P， Wang B H， Chen X G and Jin L J. 2012. Damaged cables recognition based on improved Freeman rule. Journal of Mechanical and Electrical Engineering， 29（2）： 211-214
刘鲲鹏， 王滨海， 陈西广， 金立军. 2012. 基于Freeman改进准则的输电线断股识别. 机电工程， 29（2）： 211-214 ［DOI： 10.3969/j.issn.1001-4551.2012.02.022http://dx.doi.org/10.3969/j.issn.1001-4551.2012.02.022］
Liu Z， Zhang L M， Geng M X， Yao J， Zhang J L and Hu Y F. 2019. Object detection of high-voltage cable based on improved Faster R-CNN. CAAI Transactions on Intelligent Systems， 14（4）： 627-634
刘召， 张黎明， 耿美晓， 么军， 张金禄， 胡益菲. 2019. 基于改进的Faster R-CNN高压线缆目标检测方法. 智能系统学报， 14（4）： 627-634 ［DOI： 10.11992/tis.201905026http://dx.doi.org/10.11992/tis.201905026］
Liu Z Y， Miao X R， Chen J and Jiang H. 2020. Review of visible image intelligent processing for transmission line inspection. Power System Technology， 44（3）： 1057-1069
刘志颖， 缪希仁， 陈静， 江灏. 2020. 电力架空线路巡检可见光图像智能处理研究综述. 电网技术， 44（3）： 1057-1069 ［DOI： 10.13335/j.1000-3673.pst.2019.0349http://dx.doi.org/10.13335/j.1000-3673.pst.2019.0349］
Luo C X， Wan H J， Luo W B and Jiang L. 2014. Image processing and recognition technology for transmission line icing. Electric Power， 47（9）： 132-136
罗朝祥， 万华舰， 罗文博， 姜岚. 2014. 输电线路导线覆冰图像处理与识别技术. 中国电力， 47（9）： 132-136 ［DOI： 10.11930/j.issn.1004-9649.2014.9.132.4http://dx.doi.org/10.11930/j.issn.1004-9649.2014.9.132.4］
Luo X Y， Zhang J， Cao X B， Yan P K and Li X L. 2014. Object-aware power line detection using color and near-infrared images. IEEE Transactions on Aerospace and Electronic Systems， 50（2）： 1374-1389 ［DOI： 10.1109/TAES.2013.120444http://dx.doi.org/10.1109/TAES.2013.120444］
Luo Y H， Yu X and Yang D S. 2021. A new recognition algorithm for high-voltage lines based on improved LSD and convolutional neural networks. IET Image Processing， 15（1）： 260-268 ［DOI： 10.1049/ipr2.12031http://dx.doi.org/10.1049/ipr2.12031］
Ma W F， Wang C， Wang J L， Zhou J C and Ma Y Y. 2020. Extraction of power lines from laser point cloud based on residual clustering method. Acta Geodaetica et Cartographica Sinica， 49（7）： 883-892
麻卫峰， 王成， 王金亮， 周京春， 麻源源. 2020. 激光点云输电线精细提取的残差聚类法. 测绘学报， 49（7）： 883-892 ［DOI： 10.11947/j.AGCS.2020.20190373http://dx.doi.org/10.11947/j.AGCS.2020.20190373］
Madaan R， Maturana D and Scherer S. 2017. Wire detection using synthetic data and dilated convolutional networks for unmanned aerial vehicles//Proceedings of 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems （IROS）. Vancouver， Canada： IEEE： 3487-3494 ［DOI： 10.1109/IROS.2017.8206190http://dx.doi.org/10.1109/IROS.2017.8206190］
Menendez O A， Perez M and Cheein F A A. 2016. Vision based inspection of transmission lines using unmanned aerial vehicles//Proceedings of 2016 International Conference on Multisensor Fusion and Integration for Intelligent Systems （MFI）. Baden-Baden， Germany： IEEE： 412-417 ［DOI： 10.1109/MFI.2016.7849523http://dx.doi.org/10.1109/MFI.2016.7849523］
Miao X R， Liu X Y， Chen J， Zhuang S B， Fan J W and Jiang H. 2019. Insulator detection in aerial images for transmission line inspection using single shot multibox detector. IEEE Access， 7： 9945-9956 ［DOI： 10.1109/ACCESS.2019.2891123http://dx.doi.org/10.1109/ACCESS.2019.2891123］
Nasseri M H， Moradi H， Nasiri S M and Hosseini R. 2018. Power line detection and tracking using Hough transform and particle filter//Proceedings of the 6th RSI International Conference on Robotics and Mechatronics （IcRoM）. Tehran， Iran： IEEE： 130-134 ［DOI： 10.1109/ICRoM.2018.8657568http://dx.doi.org/10.1109/ICRoM.2018.8657568］
Nguyen V N， Jenssen R and Roverso D. 2018. Automatic autonomous vision-based power line inspection： a review of current status and the potential role of deep learning. International Journal of Electrical Power and Energy Systems， 99： 107-120 ［DOI： 10.1016/j.ijepes.2017.12.016http://dx.doi.org/10.1016/j.ijepes.2017.12.016］
Nguyen V N， Jenssen R and Roverso D. 2021. LS-Net： fast single-shot line-segment detector. Machine Vision and Applications， 32（1）： #12 ［DOI： 10.1007/s00138-020-01138-6http://dx.doi.org/10.1007/s00138-020-01138-6］
Pan C F， Cao X B and Wu D P. 2016. Power line detection via background noise removal//Proceedings of 2016 IEEE Global Conference on Signal and Information Processing （GlobalSIP）. Washington， USA： IEEE： 871-875 ［DOI： 10.1109/GlobalSIP.2016.7905967http://dx.doi.org/10.1109/GlobalSIP.2016.7905967］
Pan D F and Wang B. 2008. Line extraction based on edge direction. Transactions of Beijing Institute of Technology， 28（6）： 513-516
潘大夫， 汪渤. 2008. 基于边缘方向的直线提取算法. 北京理工大学学报， 28（6）： 513-516
Qi Y C， Wu X L， Zhao Z B， Shi B Q and Nie L Q. 2021. Bolt defect detection for aerial transmission lines using Faster R-CNN with an embedded dual attention mechanism. Journal of Image and Graphics， 26（11）： 2594-2604
戚银城， 武学良， 赵振兵， 史博强， 聂礼强. 2021. 嵌入双注意力机制的Faster R-CNN航拍输电线路螺栓缺陷检测. 中国图象图形学报， 26（11）： 2594-2604 ［DOI： 10.11834/jig.200793http://dx.doi.org/10.11834/jig.200793］
Ronneberger O， Fischer P and Brox T. 2015. U-Net： convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich， Germany： Springer： 234-241 ［DOI： 10.1007/978-3-319-24574-4_28http://dx.doi.org/10.1007/978-3-319-24574-4_28］
Saurav S， Gidde P， Singh S and Saini R. 2019. Power line segmentation in aerial images using convolutional neural networks//Proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence. Tezpur， India： Springer： 623-632 ［DOI： 10.1007/978-3-030-34869-4_68http://dx.doi.org/10.1007/978-3-030-34869-4_68］
Shan H T， Zhang J， Cao X B， Li X L and Wu D P. 2017. Multiple auxiliaries assisted airborne power line detection. IEEE Transactions on Industrial Electronics， 64（6）： 4810-4819 ［DOI： 10.1109/TIE.2017.2668994http://dx.doi.org/10.1109/TIE.2017.2668994］
Shelhamer E， Long J and Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence， 39（4）： 640-651 ［DOI： 10.1109/TPAMI.2016.2572683http://dx.doi.org/10.1109/TPAMI.2016.2572683］
Shi P J， Fang Y H， Lin C D， Liu Y F and Zhai R F. 2015. A new line detection algorithm——Automatic measurement of character parameter of rapeseed plant by LSD//Proceedings of the 4th International Conference on Agro-Geoinformatics. Istanbul， Turkey： IEEE： 257-262 ［DOI： 10.1109/Agro-Geoinformatics.2015.7248122http://dx.doi.org/10.1109/Agro-Geoinformatics.2015.7248122］
Shuai C， Wang H L， Zhang G F， Kou Z and Zhang W. 2017. Power lines extraction and distance measurement from binocular aerial images for power lines inspection using UAV//Proceedings of the 9th International Conference on Intelligent Human-Machine Systems and Cybernetics （IHMSC）. Hangzhou， China： IEEE： 69-74 ［DOI： 10.1109/IHMSC.2017.131http://dx.doi.org/10.1109/IHMSC.2017.131］
Song B Q and Li X L. 2014. Power line detection from optical images. Neurocomputing， 129： 350-361 ［DOI： 10.1016/j.neucom.2013.09.023http://dx.doi.org/10.1016/j.neucom.2013.09.023］
Sui Y， Ning P F， Niu P J， Wang C Y， Zhao D， Zhang W L， Han S Z， Liang L J， Xue G J and Cui Y J. 2021. Review on mounted UAV for transmission line inspection. Power System Technology， 45（9）： 3636-3648
隋宇， 宁平凡， 牛萍娟， 王辰羽， 赵地， 张伟龙， 韩抒真， 梁立君， 薛高建， 崔颜军. 2021. 面向架空输电线路的挂载无人机电力巡检技术研究综述. 电网技术， 45（9）： 3636-3648 ［DOI： 10.13335/j.1000-3673.pst.2020.1178http://dx.doi.org/10.13335/j.1000-3673.pst.2020.1178］
Tan L， Wang Y N and Shen C S. 2011. Vision based obstacle detection and recognition algorithm for transmission line deicing robot. Chinese Journal of Scientific Instrument， 32（11）： 2564-2571
谭磊， 王耀南， 沈春生. 2011. 输电线路除冰机器人障碍视觉检测识别算法. 仪器仪表学报， 32（11）： 2564-2571 ［DOI： 10.19650/j.cnki.cjsi.2011.11.025http://dx.doi.org/10.19650/j.cnki.cjsi.2011.11.025］
Tong W G， Liu S B and Sun Y M. 2016. Broken strands detection of transmission line based on chain code and texture analysis. Computer Technology and Development， 26（11）： 139-143
仝卫国， 刘士波， 孙艺萌. 2016. 基于链码和纹理分析的输电线断股检测. 计算机技术与发展， 26（11）： 139-143 ［DOI： 10.3969/j.issn.1673-629X.2016.11.031http://dx.doi.org/10.3969/j.issn.1673-629X.2016.11.031］
von Gioi R G， Jakubowicz J， Morel J M and Randall G. 2010. LSD： a fast line segment detector with a false detection control. IEEE Transactions on Pattern Analysis and Machine Intelligence， 32（4）： 722-732 ［DOI： 10.1109/TPAMI.2008.300http://dx.doi.org/10.1109/TPAMI.2008.300］
Wang L J， Chen Z L， Hua D and Zheng Z X. 2019. Semantic segmentation of transmission lines and their accessories based on UAV-taken images. IEEE Access， 7： 80829-80839 ［DOI： 10.1109/ACCESS.2019.2923024http://dx.doi.org/10.1109/ACCESS.2019.2923024］
Wang W Y， Liu Y J and Wang X P. 2017a. Study on extraction method of overhead power line based on prior knowledge with single visible light camera//Proceedings of the 5th International Conference on Mechatronics， Materials， Chemistry and Computer Engineering. Chongqing， China： Atlantis Press： 1439-1443 ［DOI： 10.2991/icmmcce-17.2017.249http://dx.doi.org/10.2991/icmmcce-17.2017.249］
Wang W Y， Liu Y J， Zhao L and Wang X P. 2017b. Study on algorithms of power line extraction based on power line image feature//Proceedings of the 3rd Annual International Conference on Electronics， Electrical Engineering and Information Science （EEEIS 2017）. Rome， Italy： Atlantis Press： 240-244 ［DOI： 10.2991/eeeis-17.2017.33http://dx.doi.org/10.2991/eeeis-17.2017.33］
Wang X W. 2019. Research on Semantic Segmentation of Power Line Based on Image. Hangzhou： Zhejiang University
王栩文. 2019. 基于图像的输电线路语义分割技术研究. 杭州： 浙江大学
Wu D L， Li B F， Li W T， Xia Y and Tang Y D. 2013. A vision-based system for power transmission facilities detection. Applied Mechanics and Materials， 423-426： 2547-2554 ［DOI： 10.4028/www.scientific.net/AMM.423-426.2547http://dx.doi.org/10.4028/www.scientific.net/AMM.423-426.2547］
Xu G and Li G. 2021. Research on lightweight neural network of aerial powerline image segmentation. Journal of Image and Graphics， 26（11）： 2605-2618
许刚， 李果. 2021. 轻量化航拍图像电力线语义分割. 中国图象图形学报， 26（11）： 2605-2618 ［DOI： 10.11834/jig.200690http://dx.doi.org/10.11834/jig.200690］
Xu J， Han J， Tong Z G and Wang Y X. 2017. Method for detecting bird’s nest on tower based on UAV image. Computer Engineering and Applications， 53（6）： 231-235
徐晶， 韩军， 童志刚， 王亚先. 2017. 一种无人机图像的铁塔上鸟巢检测方法. 计算机工程与应用， 53（6）： 231-235 ［DOI： 10.3778/j.issn.1002-8331.1508-0104http://dx.doi.org/10.3778/j.issn.1002-8331.1508-0104］
Xu S Z and Hu H F. 2014. Detection of power lines based on chaotic particle swarm optimization. Journal of South-Central University for Nationalities （Natural Science Edition）， 33（3）： 100-104
徐胜舟， 胡怀飞. 2014. 基于混沌粒子群优化算法的电力线检测. 中南民族大学学报（自然科学版）， 33（3）： 100-104 ［DOI： 10.3969/j.issn.1672-4321.2014.03.025http://dx.doi.org/10.3969/j.issn.1672-4321.2014.03.025］
Yan G J， Li C Y， Zhou G Q， Zhang W M and Li X W. 2007. Automatic extraction of power lines from aerial images. IEEE Geoscience and Remote Sensing Letters， 4（3）： 387-391 ［DOI： 10.1109/LGRS.2007.895714http://dx.doi.org/10.1109/LGRS.2007.895714］
Yang H. 2014. Design and Implementation of Power Line Extraction and Recognition Algorithm Based on Edge Detection. Changsha： Hunan University
杨辉. 2014. 基于边缘检测的电力线检测系统的设计与实现. 长沙： 湖南大学
Yang L， Fan J F， Liu Y H， Li E， Peng J Z and Liang Z Z. 2020. A review on state-of-the-art power line inspection techniques. IEEE Transactions on Instrumentation and Measurement， 69（12）： 9350-9365 ［DOI： 10.1109/TIM.2020.3031194http://dx.doi.org/10.1109/TIM.2020.3031194］
Yetgin Ö E， Benligiray B and Gerek Ö N. 2019. Power line recognition from aerial images with deep learning. IEEE Transactions on Aerospace and Electronic Systems， 55（5）： 2241-2252 ［DOI： 10.1109/TAES.2018.2883879http://dx.doi.org/10.1109/TAES.2018.2883879］
Yuan C X， Guan Y L， Zhang J J and Yuan C H. 2018. Power line extraction based on improved Hough transform. Beijing Surveying and Mapping， 32（6）： 730-733
袁晨鑫， 官云兰， 张晶晶， 袁晨瀚. 2018. 基于改进Hough变换的电力线提取. 北京测绘， 32（6）： 730-733 ［DOI： 10.19580/j.cnki.1007-3000.2018.06.023http://dx.doi.org/10.19580/j.cnki.1007-3000.2018.06.023］
Zhai Y J， Chen R， Yang Q， Li X X and Zhao Z B. 2018. Insulator fault detection based on spatial morphological features of aerial images. IEEE Access， 6： 35316-35326 ［DOI： 10.1109/ACCESS.2018.2846293http://dx.doi.org/10.1109/ACCESS.2018.2846293］
Zhai Y J， Yang X， Wang Q M， Zhao Z B and Zhao W Q. 2021. Hybrid knowledge R-CNN for transmission line multifitting detection. IEEE Transactions on Instrumentation and Measurement， 70： #5013312 ［DOI： 10.1109/TIM.2021.3096600http://dx.doi.org/10.1109/TIM.2021.3096600］
Zhang C X， Zhao L and Wang X P. 2018. Research on fast extraction algorithm of power line in complex ground object background. Engineering Journal of Wuhan University， 51（8）： 732-739
张从新， 赵乐， 王先培. 2018. 复杂地物背景下电力线的快速提取算法. 武汉大学学报（工学版）， 2018， 51（8）： 732-739 ［DOI： 10.14188/j.1671-8844.2018-08-011http://dx.doi.org/10.14188/j.1671-8844.2018-08-011］
Zhang H， Yang W， Yu H， Zhang H J and Xia G S. 2019. Detecting power lines in UAV images with convolutional features and structured constraints. Remote Sensing， 11（11）： #1342 ［DOI： 10.3390/rs11111342http://dx.doi.org/10.3390/rs11111342］
Zhang J， Shan H T， Cao X B， Yan P K and Li X L. 2014. Pylon line spatial correlation assisted transmission line detection. IEEE Transactions on Aerospace and Electronic Systems， 50（4）： 2890-2905 ［DOI： 10.1109/TAES.2014.120732http://dx.doi.org/10.1109/TAES.2014.120732］
Zhang J J， Liu L， Wang B H， Chen X G， Wang Q and Zheng T R. 2012. High speed automatic power line detection and tracking for a UAV-based inspection//Proceedings of 2012 International Conference on Industrial Control and Electronics Engineering. Xi’an， China： IEEE： 266-269 ［DOI： 10.1109/ICICEE.2012.77http://dx.doi.org/10.1109/ICICEE.2012.77］
Zhang M， Khalid A A， Li Y F and Chen Y. 2020. Automatic detection of transmission line on UAV inspection images with the statistics approach in the DCT domain//Proceedings of 2020 International Conference on Sensing， Measurement and Data Analytics in the Era of Artificial Intelligence （ICSMD）. Xi’an， China： IEEE： 577-581 ［DOI： 10.1109/ICSMD50554.2020.9261633http://dx.doi.org/10.1109/ICSMD50554.2020.9261633］
Zhang Y P， Wang W H， Zhao S P and Zhao S X. 2022. Research on automatic extraction of railway catenary PowerLines under complex background based on RBCT algorithm. High Voltage Engineering， 48（6）： 2234-2243
张友鹏， 王文豪， 赵珊鹏， 赵少翔. 2022. 基于RBCT算法的复杂背景下铁路接触网电力线自动提取研究. 高电压技术， 48（6）： 2234-2243 ［DOI： 10.13336/j.1003-6520.hve.20210661http://dx.doi.org/10.13336/j.1003-6520.hve.20210661］
Zhao H C， Lei J F， Wang X P， Zhao L， Tian M， Cao W B， Yao H T and Cai B B. 2019. Power line identification algorithm for aerial image in complex background. Bulletin of Surveying and Mapping， （7）： 28-32
赵浩程， 雷俊峰， 王先培， 赵乐， 田猛， 曹文彬， 姚鸿泰， 蔡兵兵. 2019. 背景复杂下航拍图像的电力线识别算法. 测绘通报， （7）： 28-32 ［DOI： 10.13474/j.cnki.11-2246.2019.0213http://dx.doi.org/10.13474/j.cnki.11-2246.2019.0213］
Zhao H S， Shi J P， Qi X J， Wang X G and Jia J Y. 2017. Pyramid scene parsing network//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. Honolulu， USA： IEEE： 6230-6239 ［DOI： 10.1109/CVPR.2017.660http://dx.doi.org/10.1109/CVPR.2017.660］
Zhao L， Wang X P， Dai D D， Long J C， Tian M and Zhu G W. 2019. Automatic extraction algorithm of power line in complex background. High Voltage Engineering， 45（1）： 218-227
赵乐， 王先培， 代荡荡， 龙嘉川， 田猛， 朱国威. 2019. 复杂背景下电力线自动提取算法. 高电压技术， 45（1）： 218-227 ［DOI： 10.13336/j.1003-6520.hve.20180913001http://dx.doi.org/10.13336/j.1003-6520.hve.20180913001］
Zhao L， Wang X P， Yao H T and Tian M. 2021a. Survey of power line extraction methods based on visible light aerial image. Power System Technology， 45（4）： 1536-1546
赵乐， 王先培， 姚鸿泰， 田猛. 2021a. 基于可见光航拍图像的电力线提取算法综述. 电网技术， 45（4）： 1536-1546 ［DOI： 10.13335/j.1000-3673.pst.2020.0300ahttp://dx.doi.org/10.13335/j.1000-3673.pst.2020.0300a］
Zhao L， Wang X P， Yao H T， Tian M and Gong L. 2021b. Power line extraction algorithm based on local context information. High Voltage Engineering， 47（7）： 2553-2563
赵乐， 王先培， 姚鸿泰， 田猛， 龚立. 2021b. 基于局部上下文信息的电力线提取算法. 高电压技术， 47（7）： 2553-2563 ［DOI： 10.13336/j.1003-6520.hve.20200048http://dx.doi.org/10.13336/j.1003-6520.hve.20200048］
Zhao L P， Fan H J， Zhu L L and Tang Y D. 2012. Research on real-time detection and recognition algorithm of high-voltage transmission line for inspection with unmanned aerial vehicle. Journal of Chinese Computer Systems， 33（4）： 882-886
赵利坡， 范慧杰， 朱琳琳， 唐延东. 2012. 面向巡线无人机高压线实时检测与识别算法. 小型微型计算机系统， 33（4）： 882-886 ［DOI： 10.3969/j.issn.1000-1220.2012.04.043http://dx.doi.org/10.3969/j.issn.1000-1220.2012.04.043］
Zhao Z B， Jiang Z G， Li Y X， Qi Y C， Zhai Y J， Zhao W Q and Zhang K. 2021. Overview of visual defect detection of transmission line components. Journal of Image and Graphics， 26（11）： 2545-2560
赵振兵， 蒋志钢， 李延旭， 戚银城， 翟永杰， 赵文清， 张珂. 2021. 输电线路部件视觉缺陷检测综述. 中国图象图形学报， 26（11）： 2545-2560 ［DOI： 10.11834/jig.200689http://dx.doi.org/10.11834/jig.200689］
Zhao Z B， Qi H Y， Qi Y C， Zhang K， Zhai Y J and Zhao W Q. 2020. Detection method based on automatic visual shape clustering for pin-missing defect in transmission lines. IEEE Transactions on Instrumentation and Measurement， 69（9）： 6080-6091 ［DOI： 10.1109/TIM.2020.2969057http://dx.doi.org/10.1109/TIM.2020.2969057］
Zhao Z B， Wang Q and Gao Q. 2011. Analysis and extraction of power line image using improved phase congruency. High Voltage Engineering， 37（8）： 2004-2009
赵振兵， 王琴， 高强. 2011. 采用改进相位一致性检测方法的电力线图像分析及其提取. 高电压技术， 37（8）： 2004-2009 DOI： 10.13336/j.1003-6520.hve.2011.08.004http://dx.doi.org/10.13336/j.1003-6520.hve.2011.08.004］
Zhao Z B， Xiong J， Li B， Wang Y R and Zhang S. 2022. Typical fittings and its partial defect detection method based on improved cascade R-CNN. High Voltage Engineering， 48（3）： 1060-1067
赵振兵， 熊静， 李冰， 王亚茹， 张帅. 2022. 基于改进Cascade R-CNN的典型金具及其部分缺陷检测方法. 高电压技术， 48（3）： 1060-1067 ［DOI： 10.13336/j.1003-6520.hve.20211148http://dx.doi.org/10.13336/j.1003-6520.hve.20211148］
Zhou D X and Zhang H. 2005. Modified GMM background modeling and optical flow for detection of moving objects//Proceedings of 2005 IEEE International Conference on Systems， Man and Cybernetics. Waikoloa， USA： IEEE： 2224-2229 ［DOI： 10.1109/ICSMC.2005.1571479http://dx.doi.org/10.1109/ICSMC.2005.1571479］
Zhu L L， Cao W R， Han J D and Du Y K. 2013. A double-side filter based power line recognition method for UAV vision system//Proceedings of 2013 IEEE International Conference on Robotics and Biomimetics （ROBIO）. Shenzhen， China： IEEE： 2655-2660 ［DOI： 10.1109/ROBIO.2013.6739874http://dx.doi.org/10.1109/ROBIO.2013.6739874］
相关文章
相关作者
相关机构
京公网安备11010802024621