Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing

Adverse road condition is the main cause of traffic accidents. Road surface condition recognition based on video image has become a central issue. However, hybrid road surface and road surface under different lighting environments are two crucial problems. In this paper, the road surface states are categorized into 5 types including dry, wet, snow, ice, and water. Then, according to the original image size, images are segmented; 9-dimensional color eigenvectors and 4 texture eigenvectors are extracted to construct road surface state characteristics database. Next, a recognition method of road surface state based on SVM (Support Vector Machine) is proposed. In order to improve the recognition accuracy and the universality, a grid searching algorithm and PSO (Particle Swarm Optimization) algorithm are used to optimize the kernel function factor and penalty factor of SVM. Finally, a large number of actual road surface images in different environments are tested. The results show that the method based on SVM and image segmentation is feasible. The accuracy of PSO algorithm is more than 90%, which effectively solves the problem of road surface state recognition under the condition of hybrid or different video scenes.

• Record URL:
  https://doi.org/10.1155/2017/6458495
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
• Supplemental Notes:
  • © 2017 Jiandong Zhao et al.
• Authors:
  • Zhao, Jingyao
  • Wu, H
  • Chen, Long
• Publication Date: 2017

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • Journal of Advanced Transportation
  • Volume: 2017
  • Issue Number: Article ID 6458495
  • Publisher: John Wiley & Sons, Incorporated
  • ISSN: 0197-6729
  • EISSN: 2042-3195
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Algorithms; Data collection; Image analysis; Image processing; Roughness; Surface course (Pavements); Video; Weather conditions
• Subject Areas: Highways; Pavements; Safety and Human Factors;

Filing Info

• Accession Number: 01666119
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 16 2018 11:22AM