Abstract

The increasing train speed on railways generates an urgent need for more powerful automatic inspection of railway tracks, including real-time fastening component inspection. To obtain better high-speed performance with lower cost, this paper has proposed a novel structured light method based on motion image (SLMMI) for moving object inspection. The motion images in the proposed method are insensitive to motion, abundant with information, and easy to process, resulting in a low performance requirement of the hardware. Compared to the conventional unstructured light method and structured light method, the proposed method inherits the virtues of both thus offering a fresh perspective when inspecting missing fastening components on high-speed railways. By using the SLMMI and the recognition method based on a neural network, the experimental results yield good performance in terms of speed and accuracy. Furthermore, the robustness of the proposed method is also discussed and simulated by adding typical interferences, such as ambient light, vibration, and obstacles.

© 2011 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. C. Mair and S. Fararooy, “Practice and potential of computer vision for railways,” in IEE Seminar on Condition Monitoring for Rail Transport Systems (IEEE, 1998), pp. 101–103.
  2. S. V. Sawadisavi, R. Edwards, E. Resendiz, J. M. Hart, C. P. L. Barkan, and N. Ahuja, “Machine-vision inspection of railroad track,” presented at the Transportation Research Board 88th Annual Meeting, Washington, DC, (11–15 Jan. 2009).
  3. F. Marino, A. Distante, M. P. Luigi, and E. Stella, “A real-time visual inspection system for railway maintenance: automatic hexagonal-headed bolts detection,” IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 37, 418–428 (2007).
    [Crossref]
  4. P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of fastening bolts for railroad maintenance,” Pattern Recogn. Lett. 25, 669–677 (2004).
    [Crossref]
  5. H. Hsieh, N. Chen, and C. Liao, “Visual recognition system of elastic rail clips for mass rapid transit systems,” in Proceedings of ASME/IEEE 2007 Joint Rail Conference and Internal Combustion Engine Division Spring Technical Conference (American Society of Mechanical Engineering, 2007), pp. 319–325.
    [Crossref] [PubMed]
  6. P. De Ruvo, A. Distante, E. Stella, and F. Marino, “A GPU-based vision system for real time detection of fastening elements in railway inspection,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2009), pp. 2333–2336.
  7. P. L. Mazzco, E. Stella, N. Ancona, and A. Distante, “Visual detection of hexagonal headed bolts using method of frames and matching pursuit,” in Second Iberian Conference on Pattern Recognition and Image Analysis (Springer, 2005), pp. 277–284.
  8. G. De Ruvo, P. De Ruvo, F. Marino, G. Mastronardi, P. L. Mazzeo, and E. Stella, “A FPGA-based architecture for automatic hexagonal bolts detection in railway maintenance,” in Seventh International Workshop on Computer Architecture for Machine Perception (Computer Architecture for Machine Perception, 2005), pp. 219–224.
    [Crossref]
  9. P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of noisy fastening bolts using neural networks and wavelet transform,” in Proceedings of the Fourth International Conference on Visualization, Imaging, and Image Processing (Acta, 2004), pp. 566–571.
  10. ENSCO Inc., “Track geometry measurement system,” http://www.ensco.com/index.cfm?page=414.
  11. C. Alippi, E. Casagrande, M. Fumagalli, F. Scotti, V. Piuri, and L. Valsecchi, “An embedded system methodology for real-time analysis of railways track profile,” in Proceedings of IEEE Conference on Instrumentation and Measurement Technology (IEEE, 2002), pp. 747–751.
  12. C. M. Villar, I. J. A. Nagle, and S. C. Orrell, “Tilt correction system and method for rail seat abrasion,” U.S. patent 12/489,570 (24 Dec. 2009).
  13. S. Zhang, Jinghu Gaosu Tielu Xitong Youhua Yanjiu (China Railway, 2009).
  14. M. T. C. Musavi, K. H. Hummels, and D. M. Kalantri, “On the generalization ability of neural network classifiers,” IEEE Trans. Pattern Anal. Machine Intell. 16, 659–663 (1994).
    [Crossref]
  15. G. P. Zhang, “Neural networks for classification: a survey,” IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 30, 451–462 (2000).
    [Crossref]
  16. C. Wei-Ge, N. Nandhakumar, and W. N. Martin, “Image motion estimation from motion smear-a new computational model,” IEEE Trans. Pattern Anal. Machine Intell. 18, 412–425 (1996).
    [Crossref]
  17. R. C. Gonzalez and R. E. Woods, Digital Image Processing (Publishing House of Electronics Industry, 2002).
  18. D. J. C. MacKay, “Bayesian interpolation,” Neural Comput. 4, 415–447 (1992).
    [Crossref]
  19. H. Zhang, Department of Instrument Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China, W. Tao, M. Liu, and H. Zhao are preparing a manuscript to be called “Laser scanning system for fastener inspection.”
  20. H. Zhang, J. Yang, W. Tao, and H. Zhao, “Low-cost fastener inspection system on high-speed railway,” in IEEE International Conference on Test and Measurement (IEEE, 2010), pp. 345–347.

2010 (1)

H. Zhang, J. Yang, W. Tao, and H. Zhao, “Low-cost fastener inspection system on high-speed railway,” in IEEE International Conference on Test and Measurement (IEEE, 2010), pp. 345–347.

2009 (2)

S. Zhang, Jinghu Gaosu Tielu Xitong Youhua Yanjiu (China Railway, 2009).

P. De Ruvo, A. Distante, E. Stella, and F. Marino, “A GPU-based vision system for real time detection of fastening elements in railway inspection,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2009), pp. 2333–2336.

2007 (2)

F. Marino, A. Distante, M. P. Luigi, and E. Stella, “A real-time visual inspection system for railway maintenance: automatic hexagonal-headed bolts detection,” IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 37, 418–428 (2007).
[Crossref]

H. Hsieh, N. Chen, and C. Liao, “Visual recognition system of elastic rail clips for mass rapid transit systems,” in Proceedings of ASME/IEEE 2007 Joint Rail Conference and Internal Combustion Engine Division Spring Technical Conference (American Society of Mechanical Engineering, 2007), pp. 319–325.
[Crossref] [PubMed]

2005 (2)

P. L. Mazzco, E. Stella, N. Ancona, and A. Distante, “Visual detection of hexagonal headed bolts using method of frames and matching pursuit,” in Second Iberian Conference on Pattern Recognition and Image Analysis (Springer, 2005), pp. 277–284.

G. De Ruvo, P. De Ruvo, F. Marino, G. Mastronardi, P. L. Mazzeo, and E. Stella, “A FPGA-based architecture for automatic hexagonal bolts detection in railway maintenance,” in Seventh International Workshop on Computer Architecture for Machine Perception (Computer Architecture for Machine Perception, 2005), pp. 219–224.
[Crossref]

2004 (2)

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of noisy fastening bolts using neural networks and wavelet transform,” in Proceedings of the Fourth International Conference on Visualization, Imaging, and Image Processing (Acta, 2004), pp. 566–571.

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of fastening bolts for railroad maintenance,” Pattern Recogn. Lett. 25, 669–677 (2004).
[Crossref]

2002 (2)

C. Alippi, E. Casagrande, M. Fumagalli, F. Scotti, V. Piuri, and L. Valsecchi, “An embedded system methodology for real-time analysis of railways track profile,” in Proceedings of IEEE Conference on Instrumentation and Measurement Technology (IEEE, 2002), pp. 747–751.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Publishing House of Electronics Industry, 2002).

2000 (1)

G. P. Zhang, “Neural networks for classification: a survey,” IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 30, 451–462 (2000).
[Crossref]

1998 (1)

C. Mair and S. Fararooy, “Practice and potential of computer vision for railways,” in IEE Seminar on Condition Monitoring for Rail Transport Systems (IEEE, 1998), pp. 101–103.

1996 (1)

C. Wei-Ge, N. Nandhakumar, and W. N. Martin, “Image motion estimation from motion smear-a new computational model,” IEEE Trans. Pattern Anal. Machine Intell. 18, 412–425 (1996).
[Crossref]

1994 (1)

M. T. C. Musavi, K. H. Hummels, and D. M. Kalantri, “On the generalization ability of neural network classifiers,” IEEE Trans. Pattern Anal. Machine Intell. 16, 659–663 (1994).
[Crossref]

1992 (1)

D. J. C. MacKay, “Bayesian interpolation,” Neural Comput. 4, 415–447 (1992).
[Crossref]

Ahuja, N.

S. V. Sawadisavi, R. Edwards, E. Resendiz, J. M. Hart, C. P. L. Barkan, and N. Ahuja, “Machine-vision inspection of railroad track,” presented at the Transportation Research Board 88th Annual Meeting, Washington, DC, (11–15 Jan. 2009).

Alippi, C.

C. Alippi, E. Casagrande, M. Fumagalli, F. Scotti, V. Piuri, and L. Valsecchi, “An embedded system methodology for real-time analysis of railways track profile,” in Proceedings of IEEE Conference on Instrumentation and Measurement Technology (IEEE, 2002), pp. 747–751.

Ancona, N.

P. L. Mazzco, E. Stella, N. Ancona, and A. Distante, “Visual detection of hexagonal headed bolts using method of frames and matching pursuit,” in Second Iberian Conference on Pattern Recognition and Image Analysis (Springer, 2005), pp. 277–284.

Barkan, C. P. L.

S. V. Sawadisavi, R. Edwards, E. Resendiz, J. M. Hart, C. P. L. Barkan, and N. Ahuja, “Machine-vision inspection of railroad track,” presented at the Transportation Research Board 88th Annual Meeting, Washington, DC, (11–15 Jan. 2009).

Casagrande, E.

C. Alippi, E. Casagrande, M. Fumagalli, F. Scotti, V. Piuri, and L. Valsecchi, “An embedded system methodology for real-time analysis of railways track profile,” in Proceedings of IEEE Conference on Instrumentation and Measurement Technology (IEEE, 2002), pp. 747–751.

Chen, N.

H. Hsieh, N. Chen, and C. Liao, “Visual recognition system of elastic rail clips for mass rapid transit systems,” in Proceedings of ASME/IEEE 2007 Joint Rail Conference and Internal Combustion Engine Division Spring Technical Conference (American Society of Mechanical Engineering, 2007), pp. 319–325.
[Crossref] [PubMed]

De Ruvo, G.

G. De Ruvo, P. De Ruvo, F. Marino, G. Mastronardi, P. L. Mazzeo, and E. Stella, “A FPGA-based architecture for automatic hexagonal bolts detection in railway maintenance,” in Seventh International Workshop on Computer Architecture for Machine Perception (Computer Architecture for Machine Perception, 2005), pp. 219–224.
[Crossref]

De Ruvo, P.

P. De Ruvo, A. Distante, E. Stella, and F. Marino, “A GPU-based vision system for real time detection of fastening elements in railway inspection,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2009), pp. 2333–2336.

G. De Ruvo, P. De Ruvo, F. Marino, G. Mastronardi, P. L. Mazzeo, and E. Stella, “A FPGA-based architecture for automatic hexagonal bolts detection in railway maintenance,” in Seventh International Workshop on Computer Architecture for Machine Perception (Computer Architecture for Machine Perception, 2005), pp. 219–224.
[Crossref]

Distante, A.

P. De Ruvo, A. Distante, E. Stella, and F. Marino, “A GPU-based vision system for real time detection of fastening elements in railway inspection,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2009), pp. 2333–2336.

F. Marino, A. Distante, M. P. Luigi, and E. Stella, “A real-time visual inspection system for railway maintenance: automatic hexagonal-headed bolts detection,” IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 37, 418–428 (2007).
[Crossref]

P. L. Mazzco, E. Stella, N. Ancona, and A. Distante, “Visual detection of hexagonal headed bolts using method of frames and matching pursuit,” in Second Iberian Conference on Pattern Recognition and Image Analysis (Springer, 2005), pp. 277–284.

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of fastening bolts for railroad maintenance,” Pattern Recogn. Lett. 25, 669–677 (2004).
[Crossref]

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of noisy fastening bolts using neural networks and wavelet transform,” in Proceedings of the Fourth International Conference on Visualization, Imaging, and Image Processing (Acta, 2004), pp. 566–571.

Edwards, R.

S. V. Sawadisavi, R. Edwards, E. Resendiz, J. M. Hart, C. P. L. Barkan, and N. Ahuja, “Machine-vision inspection of railroad track,” presented at the Transportation Research Board 88th Annual Meeting, Washington, DC, (11–15 Jan. 2009).

Fararooy, S.

C. Mair and S. Fararooy, “Practice and potential of computer vision for railways,” in IEE Seminar on Condition Monitoring for Rail Transport Systems (IEEE, 1998), pp. 101–103.

Fumagalli, M.

C. Alippi, E. Casagrande, M. Fumagalli, F. Scotti, V. Piuri, and L. Valsecchi, “An embedded system methodology for real-time analysis of railways track profile,” in Proceedings of IEEE Conference on Instrumentation and Measurement Technology (IEEE, 2002), pp. 747–751.

Gonzalez, R. C.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Publishing House of Electronics Industry, 2002).

Hart, J. M.

S. V. Sawadisavi, R. Edwards, E. Resendiz, J. M. Hart, C. P. L. Barkan, and N. Ahuja, “Machine-vision inspection of railroad track,” presented at the Transportation Research Board 88th Annual Meeting, Washington, DC, (11–15 Jan. 2009).

Hsieh, H.

H. Hsieh, N. Chen, and C. Liao, “Visual recognition system of elastic rail clips for mass rapid transit systems,” in Proceedings of ASME/IEEE 2007 Joint Rail Conference and Internal Combustion Engine Division Spring Technical Conference (American Society of Mechanical Engineering, 2007), pp. 319–325.
[Crossref] [PubMed]

Hummels, K. H.

M. T. C. Musavi, K. H. Hummels, and D. M. Kalantri, “On the generalization ability of neural network classifiers,” IEEE Trans. Pattern Anal. Machine Intell. 16, 659–663 (1994).
[Crossref]

Kalantri, D. M.

M. T. C. Musavi, K. H. Hummels, and D. M. Kalantri, “On the generalization ability of neural network classifiers,” IEEE Trans. Pattern Anal. Machine Intell. 16, 659–663 (1994).
[Crossref]

Liao, C.

H. Hsieh, N. Chen, and C. Liao, “Visual recognition system of elastic rail clips for mass rapid transit systems,” in Proceedings of ASME/IEEE 2007 Joint Rail Conference and Internal Combustion Engine Division Spring Technical Conference (American Society of Mechanical Engineering, 2007), pp. 319–325.
[Crossref] [PubMed]

Liu, M.

H. Zhang, Department of Instrument Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China, W. Tao, M. Liu, and H. Zhao are preparing a manuscript to be called “Laser scanning system for fastener inspection.”

Luigi, M. P.

F. Marino, A. Distante, M. P. Luigi, and E. Stella, “A real-time visual inspection system for railway maintenance: automatic hexagonal-headed bolts detection,” IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 37, 418–428 (2007).
[Crossref]

MacKay, D. J. C.

D. J. C. MacKay, “Bayesian interpolation,” Neural Comput. 4, 415–447 (1992).
[Crossref]

Mair, C.

C. Mair and S. Fararooy, “Practice and potential of computer vision for railways,” in IEE Seminar on Condition Monitoring for Rail Transport Systems (IEEE, 1998), pp. 101–103.

Marino, F.

P. De Ruvo, A. Distante, E. Stella, and F. Marino, “A GPU-based vision system for real time detection of fastening elements in railway inspection,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2009), pp. 2333–2336.

F. Marino, A. Distante, M. P. Luigi, and E. Stella, “A real-time visual inspection system for railway maintenance: automatic hexagonal-headed bolts detection,” IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 37, 418–428 (2007).
[Crossref]

G. De Ruvo, P. De Ruvo, F. Marino, G. Mastronardi, P. L. Mazzeo, and E. Stella, “A FPGA-based architecture for automatic hexagonal bolts detection in railway maintenance,” in Seventh International Workshop on Computer Architecture for Machine Perception (Computer Architecture for Machine Perception, 2005), pp. 219–224.
[Crossref]

Martin, W. N.

C. Wei-Ge, N. Nandhakumar, and W. N. Martin, “Image motion estimation from motion smear-a new computational model,” IEEE Trans. Pattern Anal. Machine Intell. 18, 412–425 (1996).
[Crossref]

Mastronardi, G.

G. De Ruvo, P. De Ruvo, F. Marino, G. Mastronardi, P. L. Mazzeo, and E. Stella, “A FPGA-based architecture for automatic hexagonal bolts detection in railway maintenance,” in Seventh International Workshop on Computer Architecture for Machine Perception (Computer Architecture for Machine Perception, 2005), pp. 219–224.
[Crossref]

Mazzco, P. L.

P. L. Mazzco, E. Stella, N. Ancona, and A. Distante, “Visual detection of hexagonal headed bolts using method of frames and matching pursuit,” in Second Iberian Conference on Pattern Recognition and Image Analysis (Springer, 2005), pp. 277–284.

Mazzeo, P. L.

G. De Ruvo, P. De Ruvo, F. Marino, G. Mastronardi, P. L. Mazzeo, and E. Stella, “A FPGA-based architecture for automatic hexagonal bolts detection in railway maintenance,” in Seventh International Workshop on Computer Architecture for Machine Perception (Computer Architecture for Machine Perception, 2005), pp. 219–224.
[Crossref]

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of noisy fastening bolts using neural networks and wavelet transform,” in Proceedings of the Fourth International Conference on Visualization, Imaging, and Image Processing (Acta, 2004), pp. 566–571.

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of fastening bolts for railroad maintenance,” Pattern Recogn. Lett. 25, 669–677 (2004).
[Crossref]

Musavi, M. T. C.

M. T. C. Musavi, K. H. Hummels, and D. M. Kalantri, “On the generalization ability of neural network classifiers,” IEEE Trans. Pattern Anal. Machine Intell. 16, 659–663 (1994).
[Crossref]

Nagle, I. J. A.

C. M. Villar, I. J. A. Nagle, and S. C. Orrell, “Tilt correction system and method for rail seat abrasion,” U.S. patent 12/489,570 (24 Dec. 2009).

Nandhakumar, N.

C. Wei-Ge, N. Nandhakumar, and W. N. Martin, “Image motion estimation from motion smear-a new computational model,” IEEE Trans. Pattern Anal. Machine Intell. 18, 412–425 (1996).
[Crossref]

Nitti, M.

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of fastening bolts for railroad maintenance,” Pattern Recogn. Lett. 25, 669–677 (2004).
[Crossref]

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of noisy fastening bolts using neural networks and wavelet transform,” in Proceedings of the Fourth International Conference on Visualization, Imaging, and Image Processing (Acta, 2004), pp. 566–571.

Orrell, S. C.

C. M. Villar, I. J. A. Nagle, and S. C. Orrell, “Tilt correction system and method for rail seat abrasion,” U.S. patent 12/489,570 (24 Dec. 2009).

Piuri, V.

C. Alippi, E. Casagrande, M. Fumagalli, F. Scotti, V. Piuri, and L. Valsecchi, “An embedded system methodology for real-time analysis of railways track profile,” in Proceedings of IEEE Conference on Instrumentation and Measurement Technology (IEEE, 2002), pp. 747–751.

Resendiz, E.

S. V. Sawadisavi, R. Edwards, E. Resendiz, J. M. Hart, C. P. L. Barkan, and N. Ahuja, “Machine-vision inspection of railroad track,” presented at the Transportation Research Board 88th Annual Meeting, Washington, DC, (11–15 Jan. 2009).

Sawadisavi, S. V.

S. V. Sawadisavi, R. Edwards, E. Resendiz, J. M. Hart, C. P. L. Barkan, and N. Ahuja, “Machine-vision inspection of railroad track,” presented at the Transportation Research Board 88th Annual Meeting, Washington, DC, (11–15 Jan. 2009).

Scotti, F.

C. Alippi, E. Casagrande, M. Fumagalli, F. Scotti, V. Piuri, and L. Valsecchi, “An embedded system methodology for real-time analysis of railways track profile,” in Proceedings of IEEE Conference on Instrumentation and Measurement Technology (IEEE, 2002), pp. 747–751.

Stella, E.

P. De Ruvo, A. Distante, E. Stella, and F. Marino, “A GPU-based vision system for real time detection of fastening elements in railway inspection,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2009), pp. 2333–2336.

F. Marino, A. Distante, M. P. Luigi, and E. Stella, “A real-time visual inspection system for railway maintenance: automatic hexagonal-headed bolts detection,” IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 37, 418–428 (2007).
[Crossref]

P. L. Mazzco, E. Stella, N. Ancona, and A. Distante, “Visual detection of hexagonal headed bolts using method of frames and matching pursuit,” in Second Iberian Conference on Pattern Recognition and Image Analysis (Springer, 2005), pp. 277–284.

G. De Ruvo, P. De Ruvo, F. Marino, G. Mastronardi, P. L. Mazzeo, and E. Stella, “A FPGA-based architecture for automatic hexagonal bolts detection in railway maintenance,” in Seventh International Workshop on Computer Architecture for Machine Perception (Computer Architecture for Machine Perception, 2005), pp. 219–224.
[Crossref]

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of noisy fastening bolts using neural networks and wavelet transform,” in Proceedings of the Fourth International Conference on Visualization, Imaging, and Image Processing (Acta, 2004), pp. 566–571.

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of fastening bolts for railroad maintenance,” Pattern Recogn. Lett. 25, 669–677 (2004).
[Crossref]

Tao, W.

H. Zhang, J. Yang, W. Tao, and H. Zhao, “Low-cost fastener inspection system on high-speed railway,” in IEEE International Conference on Test and Measurement (IEEE, 2010), pp. 345–347.

H. Zhang, Department of Instrument Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China, W. Tao, M. Liu, and H. Zhao are preparing a manuscript to be called “Laser scanning system for fastener inspection.”

Valsecchi, L.

C. Alippi, E. Casagrande, M. Fumagalli, F. Scotti, V. Piuri, and L. Valsecchi, “An embedded system methodology for real-time analysis of railways track profile,” in Proceedings of IEEE Conference on Instrumentation and Measurement Technology (IEEE, 2002), pp. 747–751.

Villar, C. M.

C. M. Villar, I. J. A. Nagle, and S. C. Orrell, “Tilt correction system and method for rail seat abrasion,” U.S. patent 12/489,570 (24 Dec. 2009).

Wei-Ge, C.

C. Wei-Ge, N. Nandhakumar, and W. N. Martin, “Image motion estimation from motion smear-a new computational model,” IEEE Trans. Pattern Anal. Machine Intell. 18, 412–425 (1996).
[Crossref]

Woods, R. E.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Publishing House of Electronics Industry, 2002).

Yang, J.

H. Zhang, J. Yang, W. Tao, and H. Zhao, “Low-cost fastener inspection system on high-speed railway,” in IEEE International Conference on Test and Measurement (IEEE, 2010), pp. 345–347.

Zhang, G. P.

G. P. Zhang, “Neural networks for classification: a survey,” IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 30, 451–462 (2000).
[Crossref]

Zhang, H.

H. Zhang, J. Yang, W. Tao, and H. Zhao, “Low-cost fastener inspection system on high-speed railway,” in IEEE International Conference on Test and Measurement (IEEE, 2010), pp. 345–347.

H. Zhang, Department of Instrument Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China, W. Tao, M. Liu, and H. Zhao are preparing a manuscript to be called “Laser scanning system for fastener inspection.”

Zhang, S.

S. Zhang, Jinghu Gaosu Tielu Xitong Youhua Yanjiu (China Railway, 2009).

Zhao, H.

H. Zhang, J. Yang, W. Tao, and H. Zhao, “Low-cost fastener inspection system on high-speed railway,” in IEEE International Conference on Test and Measurement (IEEE, 2010), pp. 345–347.

H. Zhang, Department of Instrument Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China, W. Tao, M. Liu, and H. Zhao are preparing a manuscript to be called “Laser scanning system for fastener inspection.”

IEEE Trans. Pattern Anal. Machine Intell. (2)

M. T. C. Musavi, K. H. Hummels, and D. M. Kalantri, “On the generalization ability of neural network classifiers,” IEEE Trans. Pattern Anal. Machine Intell. 16, 659–663 (1994).
[Crossref]

C. Wei-Ge, N. Nandhakumar, and W. N. Martin, “Image motion estimation from motion smear-a new computational model,” IEEE Trans. Pattern Anal. Machine Intell. 18, 412–425 (1996).
[Crossref]

IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. (2)

G. P. Zhang, “Neural networks for classification: a survey,” IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 30, 451–462 (2000).
[Crossref]

F. Marino, A. Distante, M. P. Luigi, and E. Stella, “A real-time visual inspection system for railway maintenance: automatic hexagonal-headed bolts detection,” IEEE Trans. Syst. Man. Cybern., Part C Appl. Rev. 37, 418–428 (2007).
[Crossref]

Neural Comput. (1)

D. J. C. MacKay, “Bayesian interpolation,” Neural Comput. 4, 415–447 (1992).
[Crossref]

Pattern Recogn. Lett. (1)

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of fastening bolts for railroad maintenance,” Pattern Recogn. Lett. 25, 669–677 (2004).
[Crossref]

Other (14)

H. Hsieh, N. Chen, and C. Liao, “Visual recognition system of elastic rail clips for mass rapid transit systems,” in Proceedings of ASME/IEEE 2007 Joint Rail Conference and Internal Combustion Engine Division Spring Technical Conference (American Society of Mechanical Engineering, 2007), pp. 319–325.
[Crossref] [PubMed]

P. De Ruvo, A. Distante, E. Stella, and F. Marino, “A GPU-based vision system for real time detection of fastening elements in railway inspection,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2009), pp. 2333–2336.

P. L. Mazzco, E. Stella, N. Ancona, and A. Distante, “Visual detection of hexagonal headed bolts using method of frames and matching pursuit,” in Second Iberian Conference on Pattern Recognition and Image Analysis (Springer, 2005), pp. 277–284.

G. De Ruvo, P. De Ruvo, F. Marino, G. Mastronardi, P. L. Mazzeo, and E. Stella, “A FPGA-based architecture for automatic hexagonal bolts detection in railway maintenance,” in Seventh International Workshop on Computer Architecture for Machine Perception (Computer Architecture for Machine Perception, 2005), pp. 219–224.
[Crossref]

P. L. Mazzeo, M. Nitti, E. Stella, and A. Distante, “Visual recognition of noisy fastening bolts using neural networks and wavelet transform,” in Proceedings of the Fourth International Conference on Visualization, Imaging, and Image Processing (Acta, 2004), pp. 566–571.

ENSCO Inc., “Track geometry measurement system,” http://www.ensco.com/index.cfm?page=414.

C. Alippi, E. Casagrande, M. Fumagalli, F. Scotti, V. Piuri, and L. Valsecchi, “An embedded system methodology for real-time analysis of railways track profile,” in Proceedings of IEEE Conference on Instrumentation and Measurement Technology (IEEE, 2002), pp. 747–751.

C. M. Villar, I. J. A. Nagle, and S. C. Orrell, “Tilt correction system and method for rail seat abrasion,” U.S. patent 12/489,570 (24 Dec. 2009).

S. Zhang, Jinghu Gaosu Tielu Xitong Youhua Yanjiu (China Railway, 2009).

H. Zhang, Department of Instrument Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China, W. Tao, M. Liu, and H. Zhao are preparing a manuscript to be called “Laser scanning system for fastener inspection.”

H. Zhang, J. Yang, W. Tao, and H. Zhao, “Low-cost fastener inspection system on high-speed railway,” in IEEE International Conference on Test and Measurement (IEEE, 2010), pp. 345–347.

R. C. Gonzalez and R. E. Woods, Digital Image Processing (Publishing House of Electronics Industry, 2002).

C. Mair and S. Fararooy, “Practice and potential of computer vision for railways,” in IEE Seminar on Condition Monitoring for Rail Transport Systems (IEEE, 1998), pp. 101–103.

S. V. Sawadisavi, R. Edwards, E. Resendiz, J. M. Hart, C. P. L. Barkan, and N. Ahuja, “Machine-vision inspection of railroad track,” presented at the Transportation Research Board 88th Annual Meeting, Washington, DC, (11–15 Jan. 2009).

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (12)

Fig. 1
Fig. 1

Schematic diagram of motion image in SLMs. (a) Inspecting system. (b) A static image of a cube. (c) A motion image of a cube. (d) Projection from the cube to motion image. (e) Projection from stairs to motion image. (f) Projection from slope to motion image.

Fig. 2
Fig. 2

Model of the SLMMI.

Fig. 3
Fig. 3

Comparison of images at different states. (a–d) Images using the SLMMI. (e–h) Images using the USLM.

Fig. 4
Fig. 4

Top-hat transformation. (a) Original image. (b) Opened image. (c) Top-hat transformation. (d) Signals at AA in (a). (e) Signals at BB in (b). (f) Signals at CC in (c).

Fig. 5
Fig. 5

(a) ROI and (b–e) horizontal projections in different states.

Fig. 6
Fig. 6

Experimental systems. (a) Real fastening system inspection. (b) High-speed experimental system.

Fig. 7
Fig. 7

Images with salt and pepper noise intensities. (a) 0.1, (b) 0.2, (c) 0.3, and (d) 0.4.

Fig. 8
Fig. 8

Errors with pattern noises of different intensities.

Fig. 9
Fig. 9

Simulated images and their projections with different inclinations. (a) Inclination of the system. (b)  20 ° , (c)  10 ° , (d)  0 ° , (e)  10 ° , and (f)  20 ° . (g–k) Corresponding projections of (b–f).

Fig. 10
Fig. 10

Errors with different inclined angles.

Fig. 11
Fig. 11

Typical obstacles and their signals in images. (a–c) Typical objects. (d–f) Corresponding signals in the motion images. (g–h) Abstracted signals.

Fig. 12
Fig. 12

Obstacle interference. (a) and (d) The images with horizontal and vertical signals. (b) and (c) Tthe average and maximum errors with different horizontal signals. (e) and (f) The average and maximum errors with different vertical signals.

Tables (2)

Tables Icon

Table 1 Comparison of Performance Requirements for Different Methods

Tables Icon

Table 2 Performances of Different Recognition Methods

Equations (8)

Equations on this page are rendered with MathJax. Learn more.

i z ( x i , y i ) = { i l ( x l , y l ) · r ( x l , y l ) , ( x i , y i ) R z 0 , ( x i , y i ) R z ,
I ( x i , y i ) = t 0 t 1 i z ( x i , y i ) d t = { t 0 t 1 i l ( x l , y l ) · r ( x l , y l ) d t , ( x i , y i ) R z 0 , ( x i , y i ) R z .
I ( x i , y i ) = { z 0 z 1 i l ( x l , y l ) · r ( x l , y l ) · d z / v , z Z x l , y l 0 , z Z x l , y l ,
I ( x i , y i ) = { i o · r o v · z 0 z 1 d z , z Z x l , y l 0 , z Z x l , y l .
I ( x i , y i ) = i l · r o · L v ,
I ( x i , y i ) = i l · r o · Δ t · L Δ z ,
I = I o ( I o b ) ,
f ( x ) = 1 1 + e x .

Metrics