Details:

Creators:
Salari, Ezzatollah ; Chou, Eddie Yein Juin ; Lynch, James ; Dutta, Utpal ;
Salari, Ezzatollah ; Chou, Eddie Yein Juin ; Lynch, James ; Dutta, Utpal ; Less -
Corporate Creators:
Michigan Ohio University Transportation Center ;
Corporate Contributors:
University of Toledo. Dept. of Electrical Engineering & Computer Science ; University of Detroit Mercy. Civil, Architectural & Environmental Engineering ; University Transportation Centers Program (U.S.) ; Michigan State Transportation Commission ; Michigan. Dept. of Transportation ; ... More +
University of Toledo. Dept. of Electrical Engineering & Computer Science ; University of Detroit Mercy. Civil, Architectural & Environmental Engineering ; University Transportation Centers Program (U.S.) ; Michigan State Transportation Commission ; Michigan. Dept. of Transportation ; United States. Federal Highway Administration ; Less -
Subject/TRT Terms:
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Image Processing
Inspection
Intelligent Transportation Systems
Pavement Distress
Pavement Maintenance
Pavement Management Systems

Publication/ Report Number:
MIOH UTC TS18p2 2010-Final ;
Resource Type:
Tech Report ;
Geographical Coverage:
Michigan ; Ohio ;
Michigan ; Ohio ; Less -
Edition:
Final report.
Corporate Publisher:
Michigan Ohio University Transportation Center ;
NTL Classification:
NTL-HIGHWAY/ROAD TRANSPORTATION-Pavement Management and Performance ; NTL-HIGHWAY/ROAD TRANSPORTATION-Construction and Maintenance ; NTL-INTELLIGENT TRANSPORTATION SYSTEMS-INTELLIGENT TRANSPORTATION SYSTEMS ;
Abstract:
The current project, funded by MIOH-UTC for the period 1/1/2009- 4/30/2010, is concerned with the development of the framework for a transportation facility inspection system using advanced image processing techniques. The focus of this study is on the technical details of investigating and utilizing state-of-the-art image analysis techniques to further advance research in image processing based inspection systems in order to detect and classify the cracks in pavement. The detection of cracks and other degradations of pavement surfaces has traditionally been done by human experts conducting visual inspection while driving along the surveyed road. This manual approach is not only time consuming but also costly and subjective. To overcome these limitations we developed two different approaches for automatic crack detection and classification to speed up the process and reduce subjectivity. In the first approach, after the pavement images are captured by a digital camera, regions corresponding to cracks are detected over the acquired images by local segmentation and then represented by a matrix of square tiles. Since the crack pattern can be represented by the distribution of the crack tiles, standard deviations of both vertical and horizontal histograms are calculated to map the cracks onto a 2D feature space, where four crack types can be identified as: longitudinal cracks, transversal cracks, block cracks and alligator cracks. This new technique provides a low-cost, near real time distress analysis option. In the second approach we explore the use of a more robust multi-resolution scheme based on the beamlet transform. This method uses a pavement distress image enhancement algorithm to correct the non-uniform background illumination by calculating the multiplicative factors that eliminate the background lighting variations. To extract the linear features such as surface cracks from the pavement images, the image is partitioned into small windows and a beamlet transform based algorithm is applied. The crack segments are then linked together and classified into four types, vertical, horizontal, transversal, and block types. Simulation results show that the method is effective and robust in the extraction of cracks from a variety of pavement images. The experimental results, obtained by testing real pavement images over local asphalt roads, present the effectiveness of our algorithm for automating the process of identifying road distresses from images.
Format:
PDF ;
Collection(s):
University Transportation Centers
US Transportation Collection
Main Document Checksum:
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urn:sha256:146687990a087cc7b223f67793944c7f8096920bb0872c2c021bde9520a7883a
File Type:

Supporting Files

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