Abstract
Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.
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References
Appelt, P.J., Goodfellow, J.W.: Research on how trees cause interruptions- applications to vegetation management. In: IEEE Rural Electric Power Conference. Scottsdale, Arizona (2004)
Beck, K., Mathieu, R.: Can power companies use space patrols to monitor transmission corridors? In: ESRI User Group Conference, San Diego (2004)
Russell, B.D.: Reliability based vegetation management through intelligent system monitoring. In: Power Systems Engineering Research Center. Texas A&M University, Texas (2007)
Berni J.A.J. et al.: Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 47(3), 722–738 (2009)
Golightly, I., Jones, D.: Visual control of an unmanned aerial vehicle for power line inspection. In: 12th International Conference on Advanced Robotics. Seattle, WA (2005)
Yan G. et al.: Automatic extraction of power lines from aerial images. IEEE Geosci. Remote Sens. Lett. 4(3), 387–391 (2007)
Ma Y. et al.: Principle and Applications of Pulse-Coupled Neural Networks. Science Press, Beijing (2005)
Eckhorn R. et al.: A neural network for feature linking via synchronous activity: results from cat visual cortex and from simulations. In: Cotterill, R.M.J. (eds) Models of Brain Function, pp. 255–272. Cambridge University Press, Cambridge (1989)
Forgáč, R., Mokriš, I.: Formal representation of images by pulse coupled neural networks. In: 3rd Slovakian–Hungarian Joint Symposium on Applied Machine Intelligence. Slovakia (2005)
Lindblad T., Kinser J.M.: Image Processing Using Pulse-Coupled Neural Networks, 2nd edn, pp. 161. Springer, Berlin (2005)
Zhang, J., Dong J., Shi, M.: An adaptive method for image filtering with pulse-coupled neural networks. In: IEEE International Conference on Image Processing (ICIP). Genova, Italy (2005)
Aggarwal N., Karl W.C.: Line detection in images through regularized Hough transform. IEEE Trans. Image Process. 15(3), 582–591 (2006)
Agganval, N., Karl, W.C.: Line detection in images through regularized hough transform. In: International Conference on Image Processing, Vancouver, BC (2000)
Fernandes L.A.F., Oliveira M.M.: Real-time line detection through an improved Hough transform voting scheme. Pattern Recognit. 41, 299–314 (2008)
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Li, Z., Liu, Y., Walker, R. et al. 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, 677–686 (2010). https://doi.org/10.1007/s00138-009-0206-y
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DOI: https://doi.org/10.1007/s00138-009-0206-y