Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning
暂无分享,去创建一个
[1] Linlin Zhu,et al. A double-side filter based power line recognition method for UAV vision system , 2013, 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO).
[2] Leon A. Gatys,et al. Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Pietro Perona,et al. One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Tsuyoshi Murata,et al. {m , 1934, ACML.
[5] Pedro B. Castellucci,et al. Pole and Crossarm Identification in Distribution Power Line Images , 2013, 2013 Latin American Robotics Symposium and Competition.
[6] Heiko Neumann,et al. Fully Convolutional Region Proposal Networks for Multispectral Person Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[7] I. Golightly,et al. Modeling and control of a robotic power line inspection vehicle , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.
[8] Yan Wang,et al. DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[10] I. Hajnsek,et al. A tutorial on synthetic aperture radar , 2013, IEEE Geoscience and Remote Sensing Magazine.
[11] Nabil Aouf,et al. Power pylon detection and monocular depth estimation from inspection UAVs , 2015, Ind. Robot.
[12] Farokh B. Bastani,et al. Robust real-time UAV based power line detection and tracking , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[13] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Przemysław Mazurek,et al. Application of background estimation and removal techniques for the extraction of the power line components on the digital images for the automatic power line inspection systems , 2008 .
[15] Richard O. Duda,et al. Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.
[16] Xiaogang Wang,et al. Factors in Finetuning Deep Model for Object Detection with Long-Tail Distribution , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Hong Luo,et al. Transmission line icing measurement on photogrammetry method , 2015, International Symposium on Multispectral Image Processing and Pattern Recognition.
[18] Francois Miralles,et al. State-of-the-art review of computer vision for the management of power transmission lines , 2014, Proceedings of the 2014 3rd International Conference on Applied Robotics for the Power Industry.
[19] Juha Hyyppä,et al. Remote sensing methods for power line corridor surveys , 2016 .
[20] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Antonios Tsourdos,et al. Development of a fuel cell hybrid-powered unmanned aerial vehicle , 2016, 2016 24th Mediterranean Conference on Control and Automation (MED).
[22] Ping Li,et al. Current trends in the development of intelligent unmanned autonomous systems , 2017, Frontiers of Information Technology & Electronic Engineering.
[23] Rushikesh K. Joshi,et al. High Level Event Ontology for Multiarea Power System , 2012, IEEE Transactions on Smart Grid.
[24] Jeremiah Neubert,et al. DEBC Detection with Deep Learning , 2017, SCIA.
[25] Dewi I. Jones,et al. Corner detection and matching for visual tracking during power line inspection , 2003, Image Vis. Comput..
[26] Xuelong Li,et al. Power line detection from optical images , 2014, Neurocomputing.
[27] Lin Wang,et al. A vision-based method for the broken spacer detection , 2015, 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER).
[28] Aamir Saeed Malik,et al. A novel method for vegetation encroachment monitoring of transmission lines using a single 2D camera , 2014, Pattern Analysis and Applications.
[29] Thomas B. Moeslund,et al. Thermal cameras and applications: a survey , 2013, Machine Vision and Applications.
[30] Chongyang Zhang,et al. A new image-based algorithm for icing detection and icing thickness estimation for Transmission Lines , 2013, 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).
[31] Wu Haibin,et al. Damper Detection in Helicopter Inspection of Power Transmission Line , 2014, 2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control.
[32] John F. Canny,et al. A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Carlos Sampedro,et al. Towards autonomous detection and tracking of electric towers for aerial power line inspection , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).
[34] Shengzhi Du,et al. Heuristic Bayesian pixel classification for power line inspection , 2010, 2010 3rd International Congress on Image and Signal Processing.
[35] Anya Castillo,et al. Risk analysis and management in power outage and restoration: A literature survey , 2014 .
[36] Lin Lei,et al. Detecting Insulators in the Image of Overhead Transmission Lines , 2012, ICIC.
[37] J. M. Lloyd,et al. Thermal Imaging Systems , 1975 .
[38] Shengzhi Du,et al. Hough Transform Tuned Bayesian Classifier for Overhead Power Line Inspection , 2009 .
[39] J. Radon. On the determination of functions from their integral values along certain manifolds , 1986, IEEE Transactions on Medical Imaging.
[40] J. Koenderink. Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.
[41] R. Ishino,et al. Detection system of damaged cables using video obtained from an aerial inspection of transmission lines , 2004, IEEE Power Engineering Society General Meeting, 2004..
[42] G. Turin,et al. An introduction to matched filters , 1960, IRE Trans. Inf. Theory.
[43] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[44] Sansanee Auephanwiriyakul,et al. Automatic detection of electricity pylons in aerial video sequences , 2010, 2010 International Conference on Electronics and Information Engineering.
[45] Xiaoxiao Li,et al. Semantic Image Segmentation via Deep Parsing Network , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[46] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[47] Shen-En Chen,et al. Laser Scanning Technology for Bridge Monitoring , 2012 .
[48] Aloysius Wehr,et al. Airborne laser scanning—an introduction and overview , 1999 .
[49] J. Katrasnik,et al. A Survey of Mobile Robots for Distribution Power Line Inspection , 2010, IEEE Transactions on Power Delivery.
[50] Jianbo Shi,et al. DeepEdge: A multi-scale bifurcated deep network for top-down contour detection , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[51] Wai Ho Li,et al. Image processing to automate condition assessment of overhead line components , 2010, 2010 1st International Conference on Applied Robotics for the Power Industry.
[52] Jinhai Cai,et al. Knowledge-based power line detection for UAV surveillance and inspection systems , 2008, 2008 23rd International Conference Image and Vision Computing New Zealand.
[53] Tanima Dutta,et al. Image Analysis-Based Automatic Detection of Transmission Towers using Aerial Imagery , 2015, IbPRIA.
[54] Qian Wang,et al. High Speed Automatic Power Line Detection and Tracking for a UAV-Based Inspection , 2012, 2012 International Conference on Industrial Control and Electronics Engineering.
[55] Georg Langs,et al. Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.
[56] Rajeev M Bhujade,et al. DETECTION OF POWER -LINES IN COMPLEX NATURAL SURROUNDINGS , 2013, ICIT 2013.
[57] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[58] Aamir Saeed Malik,et al. Vegetation encroachment monitoring for transmission lines right-of-ways: A survey , 2013 .
[59] Michael Gerke,et al. Visual inspection of power lines by U.A.S. , 2014, 2014 International Conference and Exposition on Electrical and Power Engineering (EPE).
[60] Gorka Sorrosal,et al. Automatic system for overhead power line inspection using an Unmanned Aerial Vehicle — RELIFO project , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).
[61] Sven Behnke,et al. Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks , 2016, ESANN.
[62] C. Tao,et al. Advances in Mobile Mapping Technology , 2009 .
[63] Silvio Savarese,et al. Learning Transferrable Representations for Unsupervised Domain Adaptation , 2016, NIPS.
[64] Bernardino Castillo-Toledo,et al. Power line inspection via an unmanned aerial system based on the quadrotor helicopter , 2014, MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference.
[65] Flavio Prieto,et al. Power line detection using a circle based search with UAV images , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).
[66] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[67] Bartosz Krawczyk,et al. Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.
[68] Liu Yan,et al. Power transmission tower monitoring technology based on TerraSAR-X products , 2011, International Symposium on Lidar and Radar Mapping Technologies.
[69] J. C. Dunn,et al. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .
[70] Michael Kampffmeyer,et al. Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[71] Janos Toth,et al. Smart view for a smart grid — Unmanned Aerial Vehicles for transmission lines , 2010, 2010 1st International Conference on Applied Robotics for the Power Industry.
[72] Youmin Zhang,et al. Insulator identification from aerial images using Support Vector Machine with background suppression , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).
[73] Yi Li,et al. R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.
[74] Horst Bischof,et al. Visual Recognition and Fault Detection for Power Line Insulators , 2014 .
[75] Xiaolong Wang,et al. Deep Learning for Polar Bear Detection , 2017, SCIA.
[76] Jubai An,et al. A Robust Insulator Detection Algorithm Based on Local Features and Spatial Orders for Aerial Images , 2015, IEEE Geoscience and Remote Sensing Letters.
[77] Liming Wang,et al. The Ultraviolet Detection of Corona Discharge in Power Transmission Lines , 2013 .
[78] Michael I. Jordan,et al. Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.
[79] Pascual Campoy Cervera,et al. A supervised approach to electric tower detection and classification for power line inspection , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).
[80] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[81] Hang Yin,et al. Overhead power line detection from UAV video images , 2012, 2012 19th International Conference on Mechatronics and Machine Vision in Practice (M2VIP).