Master of Science / Department of Electrical and Computer Engineering / Balasubramaniam Natarajan / In this thesis, we present an automated system for detection and classification of cracks, based on the new standard proposed by `American Association of State Highway and Transportation Officials (AASHTO)'. The AASHTO standard is a draft standard, that attempts to overcome the limitations of current crack quantifying and classification methods. In the current standard, the crack classification relies heavily on the judgment of the expert. Thus the results are susceptible to human error. The effect of human error is especially severe when the amount of data collected is large. This lead to inconsistencies even if a single standard is being followed. The new AASHTO guidelines attempt to develop a method for consistent measurement of pavement condition.
Gray scale images of the road are captured by an image capture vehicle and stored on a database. Through steps of thresholding, line detect and scanning, the gray scale image is converted to binary image, with 'zeros' representing cracked pixels. PCA analysis,
followed by closing and filtering operation, are carried out on the gray scale image to identify cracked sub-images. The output from the filtering operation, is then replaced with its binary counterpart.
In the final step the crack parameters are calculated. The region around the crack is
divided into blocks of 32x32 to approximate and calculate the crack parameters with ease.
The width of the crack is approximated by the average width of crack in each block. The
orientation of the crack is calculated from the angle between direction of travel and the line joining the ends of the crack. Length of the crack is the displacement between the ends of the crack, and the position of the crack is calculated from the midpoint of the line joining the end points.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/1920 |
| Date | January 1900 |
| Creators | Radhakrishnan, Anirudh |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0018 seconds