DOI QR코드

DOI QR Code

Dynamic PCA algorithm for Detecting Types of Electric Poles

전신주의 종류 판별을 위한 동적 PCA 알고리즘

  • 최재영 (부산대학교 전자전기공학과) ;
  • 이장명 (부산대학교 전자전기공학부)
  • Published : 2010.03.01

Abstract

This paper proposes a new dynamic PCA algorithm to recognize types of electric poles, which is necessary for a mobile robot moving along the neutral line for inspecting high-voltage facilities. Since the mobile robot needs to pass over the electric poles and grasp the neutral wire again for the next region inspection, the detection of the electric pole type is a critical factor for the successful passing-over the electric pole. The CCD camera installed on the mobile robot captures the image of the electric pole while it is approaching to the electric pole. Applying the dynamic PCA algorithm to the CCD image, the electric pole type has been classified to provide the stable grasping operation for the mobile robot. The new dynamic PCA algorithm replaces the reference image in real time to improve the robustness of the PCA algorithm, adjusts the brightness to get the clear images, and applies the Laplacian edge detection algorithm to increase the recognition rate of electric pole type. Through the real experiments, the effectiveness of this proposed dynamic PCA algorithm method using Laplacian edge detecting method has been demonstrated, which improves the recognition rate about 20% comparing to the conventional PCA algorithm.

Keywords

References

  1. Andreas Weingessel and Kurt Hornik, "Local PCA Algorithms," IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 11, NO. 6, NOVEMBER 2000.
  2. Konrad Reif, Fa-Long Luo, and Rolf Unbehauen, "An Improved Invariant-Norm PCA Algorithm with Complex Values," IEEE TENCON – Digital Signal Processing Applications, 1999
  3. Mark D. Plumbly, and Erkki Oja, "A "Nonnegative PCA" Algorithm for Independent Component Analysis," IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL.15,NO.1,JANUARY 2004.
  4. Chanchal Chatterjee, Zhengjiu Kang, and Vwani P. Roychowdhury, "Algorithms for Accelerated Convergence of Adaptive PCA," IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL.11, NO.2, MARCH 2000.
  5. J. Yang, D. Zhang, A. F. Frangi and J. Yang, "2-Dimensional PCA : A New Approach to Appearance-Based Face Representation and Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 131-137, Jan. 2004. https://doi.org/10.1109/TPAMI.2004.1261097
  6. LinLuo, M.N.S. Swamy, and I.Plotkin, "A Modified PCA Algorithm for Face Recognition," CCECE 2003 - CCGEI 2003, Montreal, May, 2003.
  7. S. Han, R. Ho, and Jang M. Lee, "Inspection of Insulators on High-Voltage Power Transmission Lines," IEEE Transaction on Power Delivery, PP. 2319-2327, Oct. 2009.
  8. M. Rizon and T. Kawaguchi, "Automatic eye detection using intensity and edge information," TENCON2000 Proceedings, vol. 2, pp. 415-420, Sept. 2000.
  9. H. Rowley, S. Baluja, and T. Kanade, "Neural Network Based Face Detection," IEEE Trans. Patt. Anal. and Machine Intell,, vol. 20, no.1, pp. 203-208,1998.