Latest revision as of 13:10, 12 February 2021

Abstract

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.

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The different versions of the original document can be found in:

• http://downloads.hindawi.com/journals/jat/2017/6458495.pdf under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.1155/2017/6458495 under the license cc-by

• http://downloads.hindawi.com/journals/jat/2017/6458495.pdf,

http://downloads.hindawi.com/journals/jat/2017/6458495.xml,

http://dx.doi.org/10.1155/2017/6458495 under the license http://creativecommons.org/licenses/by/4.0

• http://dx.doi.org/10.1155/2017/6458495,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.hindawi.com/journals/jat/2017/6458495,

http://downloads.hindawi.com/journals/jat/2017/6458495.pdf,

https://core.ac.uk/display/88193356,

https://academic.microsoft.com/#/detail/2733302282