Enhanced adaptive filter bank-based automated pavement
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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
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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.
Transcript of Enhanced adaptive filter bank-based automated pavement
The Center for Signal & Image Processing Georgia Institute of Technology
Enhanced adaptive filter-bank-based automated
pavementcrack detection and segmentation system
By
Clyde A. Lettsome, Yi-Chang Tsai, and
Vivek Kaul
The Center for Signal & Image Processing
2Outline
• Background• Design Challenges• Proposed System• Results• Conclusion
The Center for Signal & Image Processing
3Background
• 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.
The Center for Signal & Image Processing
4
• Zhou1 proposed a popular automated distress detection and segmentation structure with two main sections.
Background
The Center for Signal & Image Processing
5Background
• 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.
The Center for Signal & Image Processing
6Background
• 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.
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7Design Challenges
Pavement Distress Image Row 140 of Pavement Distress
image
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8Proposed Segmentation System
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9Proposed System: Preprocessing
• 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.
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10 Proposed System: Time-Varying Filtering
Proposed System: Time-Varying Filtering
Complimentary filters
• G00(z) low-delay lowpass filter
• G01(z) linear-phase lowpass filter
• G02(z) high-delay lowpass filter
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11Proposed System: Time-Varying Filtering
(a) Low-delay lowpass filter step response
(b) High-delay lowpass filter step response.
Why these
filters?
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12
An internal block diagram of the time-varying filtering block.
Proposed System: Time-Varying Filtering
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13Proposed System: Segmentation
An edge detection mask developed from row filtering.
A window function of Li × Li, where Li is
the length of the linear phase
filter used in the development of
the mask.
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14Proposed System: Clustering and HVS
• 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
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15Results GDOT image #1D579384
(a) Ground Truth (b) Modified filter bank (c) Li/ Mallat and Zhong
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16Results GDOT image #1D579384
(a) Ground Truth (b) Modified filter bank (c) Li/ Mallat and Zhong
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17Results S + P SPIHT Compressed Images
(a) GDOT image #1D579384 (a) GDOT image #1D579384
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18Conclusion
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.
The Center for Signal & Image Processing
19Bibliography1. 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).
