A Vision-Based System for Power Transmission Facilities Detection

Article Preview

Abstract:

In this paper, we present the design and implementation of a vision system for power transmission facilities detection based on UAV videos. The vision system consists of two main parts, the client part and the server part. The aim of the system is to detect the power transmission facilities in complex background. In order to achieve this aim, several novel methods are proposed for detecting the power transmission facilities which include the power line, the power tower, and the insulator. The experiment results on real image data demonstrate that the proposed methods are accurate and effective. The system is presented to demonstrate the performance of the detection methods.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2547-2554

Citation:

Online since:

September 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] A. M. Okamura, Methods for haptic feedback in teleoperated robot-assisted surgery, Industrial Robot: An International Journal, vol. 31, no. 6, p.499 – 508, (2004).

DOI: 10.1108/01439910410566362

Google Scholar

[2] J. Kofman, X. Wu, T. Luu, and S. Verma, Tele-operation of a robot manipulator using a vision-based human-robot interface, IEEE Trans. Ind. vol. 52, no. 5, p.1206–1219, (2005).

DOI: 10.1109/tie.2005.855696

Google Scholar

[3] W. E. Dixon, E. Zergeroglu, Y. Fang, and D. M. Dawson. Object tracking by a robot manipulator: a robust cooperative visual servoing approach. In Pmc. of the IEEE Int. Conf. on Robotics and Automation, pp.211-216, (2002).

DOI: 10.1109/robot.2002.1013363

Google Scholar

[4] Guangzhi Liu, Jianxun Li, and Zhongliang Jing. A Forward-looking Runway Recognition Algorithm Based on Improved Hough Transform. Computer Engineering, vol. 30, no. 20, pp.143-145, (2004).

Google Scholar

[5] Zhengrong Li, Yuee Liu, Hayward Ross F., Jinglan Zhang, and Jinhai Cai. Knowledge-based Power Line Detection for UAV Surveillance and InspectionSystems. In: 23rd International Conference on Image and Vision Computing New Zealand (IVCNZ 2008), 26-28 November 2008, Christchurch, New Zealand.

DOI: 10.1109/ivcnz.2008.4762118

Google Scholar

[6] Rafael Grompone von Gioi, Jérémie Jakubowicz, Jean-Michel Morel, Gregory Randall, LSD: A Fast Line Segment Detector with a False Detection Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp.722-732, (2010).

DOI: 10.1109/tpami.2008.300

Google Scholar

[7] Bingfeng Li, Denglu Wu, Cong Yang, Xia Yong, Yandong Tang, A method of insulator detection from video sequence, Proc. Int. Symp. Inf. Sci. Eng., ISISE, pp.386-389, (2012).

Google Scholar

[8] N. Otsu, A threshold selection method from gray-level histogram, IEEE Trans. Syst. Man Cybern., vol. 9, p.62–66, (1979).

DOI: 10.1109/tsmc.1979.4310076

Google Scholar

[9] Zhiming Liu, Chengjun Liu, Fusion of color, local spatial and global frequency information for face recognition, Pattern Recognition, vol. 43, no. 8, pp.2882-2890, (2010).

DOI: 10.1016/j.patcog.2010.03.003

Google Scholar

[10] Xudong Xie, Kin-Man Lam, Facial expression recognition based on shape and texture, Pattern Recognition, vol. 42, no. 5, pp.1003-1011, (2009).

DOI: 10.1016/j.patcog.2008.08.034

Google Scholar

[11] P.E. Trahanias, Binary shape recognition using the morphological skeleton transform, Pattern Recognition, vol. 25, no. 11, pp.1277-1288, (1992).

DOI: 10.1016/0031-3203(92)90141-5

Google Scholar

[12] Johan A.K. Suykens, Support Vector Machines: A Nonlinear Modelling and Control Perspective, European Journal of Control, vol. 7, pp.311-327, (2001).

DOI: 10.3166/ejc.7.311-327

Google Scholar