Enhanced adaptive filter bank-based automated pavement
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Enhanced adaptive filter bank-based automated pavement

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We incorporate, evaluate, and assess the feasibility of ...

We incorporate, evaluate, and assess the feasibility of
using filter banks in automated pavement distress systems from a
system level. We integrate a novel filter-bank-based distress segmentation
method, which, unlike previously researched methods, does not
depend on highpass data. In addition, we incorporate the standard
Said Pearlman set partitioning in hierarchical trees compression
coder into the automated pavement distress system, which is a
first in this area of research. A third contribution of the research is
a statistical detection algorithm that assists in overall system performance.
Preliminary testing using images provided by the Georgia
Department of Transportation demonstrate the promise of the proposed
method.

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Enhanced adaptive filter bank-based automated pavement Presentation Transcript

  • 1. The Center for Signal & Image Processing Georgia Institute of Technology Enhanced adaptive filter- bank-based automated pavement crack detection and segmentation system By Clyde A. Lettsome, Yi-Chang Tsai, and Vivek Kaul
  • 2. Outline 2 The Center for Signal & Image Processing• Background• Design Challenges• Proposed System• Results• Conclusion
  • 3. 3Background The Center for Signal & Image Processing• Most of the state departments of transportation (DOT) use either visual or manual distress inspection systems, which are costly, dangerous, time-consuming, labor-intensive, and subjective.• Desire – Develop effective and cheap automated pavement distress system collects pavement images or video and detects distress without human intervention.
  • 4. Background 4 The Center for Signal & Image Processing • Zhou1 proposed a popular automated distress detection and segmentation structure with two main sections.
  • 5. 5Background The Center for Signal & Image Processing• Popular Filter-bank-based systems. • Zhou1 proposed distress detection method that compared the nonzero values in the highpass subbands to predetermined thresholds. • Li2 proposed a distress segmentation method that combined threshold selection method of Mallat and Zhong3 with Gaussian filtering to remove noise and detect edges in images.• Advantage filter bank methods allow both spatial and frequency domain analysis.
  • 6. 6Background The Center for Signal & Image Processing• Disadvantages to both proposals. 1. Filter-bank decomposition, distress detection done on highpass data. Overlap and add due to row and column filtering causes construction and destruction of highpass data. 2. If standard compression coders (S+P SPIHT coder or JPEG 2000), segmentation would be performed on degraded high-low, low-high, and high-high subbands.
  • 7. 7Design Challenges The Center for Signal & Image Processing Pavement Distress Row 140 of Pavement Distress Image image
  • 8. 8Proposed Segmentation System The Center for Signal & Image Processing
  • 9. 9Proposed System: Preprocessing The Center for Signal & Image Processing •Values larger than the mean minus one standard deviation are normalized to the mean of the image. •Other values remain the same. An image preprocessed to remove surface texture.
  • 10. 10Proposed System: Time-Varying Filtering The Center for Signal & Image Processing Complimentary filters • G00(z) low-delay lowpass filter •G01(z) linear-phase lowpass filter • G02(z) high-delay lowpass filter Proposed System: Time-Varying Filtering
  • 11. 11Proposed System: Time-Varying Filtering The Center for Signal & Image Processing Why these filters? (a) Low-delay lowpass filter step response (b) High-delay lowpass filter step response.
  • 12. 12Proposed System: Time-Varying Filtering The Center for Signal & Image Processing An internal block diagram of the time-varying filtering block.
  • 13. 13Proposed System: Segmentation The Center for Signal & Image Processing A window function of Li × Li, where Li is the length of the linear phase filter used in the development of the mask.An edge detection mask developed from row filtering.
  • 14. 14Proposed System: Clustering and HVS The Center for Signal & Image Processing• Since current ground truths are determined empirically it is important to consider the human visual system (HVS).• Relationship between intensity and brightness is not linear.• Ernst Weber4 found that a perceived change in intensity occurs when
  • 15. 15Results GDOT image #1D579384 The Center for Signal & Image Processing (a) Ground Truth (b) Modified filter bank (c) Li/ Mallat and Zhong
  • 16. 16Results GDOT image #1D579384 The Center for Signal & Image Processing (a) Ground Truth (b) Modified filter bank (c) Li/ Mallat and Zhong
  • 17. 17Results S + P SPIHT Compressed Images The Center for Signal & Image Processing (a) GDOT image #1D579384 (a) GDOT image #1D579384
  • 18. 18Conclusion The Center for Signal & Image Processing  We focused on incorporating, evaluating, and assessing the feasibility of using wavelet/filter banks from a system level.  The advantage of the proposed method is that, despite the compression rate, it can be used on raw or compressed images.  The proposed system exhibited significant improvement versus existing filter-bank-based pavement distress segmentation methods.
  • 19. 19Bibliography The Center for Signal & Image Processing1. J. Zhou, P. S. Huang, and F.-P. Chiang, “Wavelet-based pavement distress detection and evaluation,” Opt. Eng. 45(2), 027007 (2006).2. J. Li, “A Wavelet Approach to Edge Detection,” Master Thesis, Mathematics Sam Houston State University, Huntsville, Texas (2003).3. S. Mallat and S. Zhong, “Characterization of signals from multiscale edges,” IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 710–732 (1992).4. M. J. T. Smith and A. Docef, A Study Guide for Digital Image Processing, Scientific Publishers Inc., Riverdale, GA (1999).