Chapter
Apr 26, 2012
The Automatic Search of Pavement Surface Distress Image Based on On-Line Learning
Authors: Xiumin Chu, Xinping Yan, and Mei LongAuthor Affiliations
Publication: International Conference on Transportation Engineering 2007
Abstract
In the paper, first the Algorithm of optimum threshold is used to remove the noise pixels of the pavement image, such as pavement marking image; Next the pavement image is divided into 400 subimages which has 100 x 100 pixels, the feature of pavement subimages are represented by gray variance, and the threshold of the good pavement subimages feature is found though the method of online learning, then the sub-images are made to do the image binarization. Finally, the counting feature of binary pavement image is used as the parameter to classify the pavement distress images, and searching pavement surface distress images are achieved.
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Published In
International Conference on Transportation Engineering 2007
Pages: 3282 - 3287
Copyright
© 2007 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Algorithms
- Automation and robotics
- Computer networks
- Computing in civil engineering
- Engineering fundamentals
- Environmental engineering
- Gravels
- Infrastructure
- Internet
- Mathematics
- Noise pollution
- Parameters (statistics)
- Pavement condition
- Pavement markings
- Pavements
- Pollution
- Statistics
- Systems engineering
- Transportation engineering
Authors
Affiliations
Xiumin Chu
ITS Center, Wuhan University of Technology, Wuhan 430063
Xinping Yan
ITS Center, Wuhan University of Technology, Wuhan 430063
Mei Long
ITS Center, Wuhan University of Technology, Wuhan 430063
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.