Enhanced adaptive filter bank-based automated pavement
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The document discusses the development of an enhanced adaptive filter-bank-based automated system for pavement crack detection and segmentation. The proposed method aims to improve the effectiveness and reduce the costs of existing inspection systems by utilizing wavelet/filter banks for image analysis, showcasing significant improvements over traditional methods. Results indicate the system's capability to operate on both raw and compressed images, addressing design challenges while considering human visual perception.
Enhanced adaptive filter bank-based automated pavement
- 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. 3 Background 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. 5 Background 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. 6 Background 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. 7 Design Challenges The Center for Signal & Image Processing Pavement Distress Row 140 of Pavement Distress Image image
- 8. 8 Proposed Segmentation System The Center for Signal & Image Processing
- 9. 9 Proposed 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. 10 Proposed 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. 11 Proposed 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. 12 Proposed System: Time-Varying Filtering The Center for Signal & Image Processing An internal block diagram of the time-varying filtering block.
- 13. 13 Proposed 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. 14 Proposed 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. 15 Results GDOT image #1D579384 The Center for Signal & Image Processing (a) Ground Truth (b) Modified filter bank (c) Li/ Mallat and Zhong
- 16. 16 Results GDOT image #1D579384 The Center for Signal & Image Processing (a) Ground Truth (b) Modified filter bank (c) Li/ Mallat and Zhong
- 17. 17 Results S + P SPIHT Compressed Images The Center for Signal & Image Processing (a) GDOT image #1D579384 (a) GDOT image #1D579384
- 18. 18 Conclusion 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. 19 Bibliography The Center for Signal & Image Processing 1. 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).