Abstract
Focusing on the existing problems of accuracy and latency in the current ice transmission line edge image detection, this chapter proposes a machine learning algorithm based on structured forest edge detection of ice transmission line. The ice transmission line image information’s model is automatically trained by the structured forest algorithm and the model is automatically applied for edge detection of the ice transmission line. Experimental results show that the method of ice transmission line edge detection is not only feasible and effective, but also can accurately detect ice line edge profile. What is more, it can meet the requirement of real time detection compared with other algorithms. Satisfactory results are obtained in the test.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yi HU, Yi H. Analysis and countermeasures discussion for large area icing accident on power grid. High Volt Eng. 2008;34(2):001–2
XU S, ZHAO J. Review of ice storm cases impacted seriously on power systems and de-icing technology. South Power Syst Technol. 2008; 2(2):002–3
Davis LS. A survey of edge detection techniques. Computer Graph Image Process. 1975;4(3):248–70.
Pal NR, Pal SK. A review on image segmentation techniques. Pattern Recognit. 1993;26(9):1277–94.
Papari G, Petkov N. Edge and line oriented contour detection: State of the art. Image Vision Comput. 2011;29(2):79–103.
Dollár P, Zitnick CL. Structured forests for fast edge detection.Computer Vision (ICCV), 2013 IEEE International Conference on. IEEE. 2013. p. 1841–48
Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
KANG Y, Nagahashi H, Sugimoto H, et al. Image categorization using scene-context scale based on random forests. IEICE Trans Inf Syst. 2011;E94-D(9):1809–16.
Yang BS, Di X, Han T. Random forests classifier for machine fault diagnosis. J Mech Sci Technol. 2008;22(9):1716–25.
Taskar B, Chatalbashev V, Koller D, et al. Learning structured prediction models: A large margin approach. Proceedings of the 22nd international conference on Machine learning (ICML), 2005 IEEE International Conference on. IEEE. 2005. p. 896–903
Bowyer K, Kranenburg C, Dougherty S. Edge detector evaluation using empirical ROC curves. IEEE computer society conference on computer vision and pattern recognition, 1999. IEEE. 1999. 1:1–3
Tan RT. Visibility in bad weather from a single image. IEEE conference on computer vision and pattern recognition, 2008. CVPR 2008. IEEE. 2008. p. 1–8
Acknowledgements
This work was supported in part by the Fundamental Research Funds for the Central Universities Grant.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Z., Jia, J., Teng, J. (2015). The Ice Edge Detection of Transmission Line Based On Structured Forest Algorithm. In: Wang, W. (eds) Proceedings of the Second International Conference on Mechatronics and Automatic Control. Lecture Notes in Electrical Engineering, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-319-13707-0_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-13707-0_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13706-3
Online ISBN: 978-3-319-13707-0
eBook Packages: EngineeringEngineering (R0)