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.
Highlights
According to statistics, 16.12% of traffic accidents on the highway are ascribed to slippery road conditions [1] since 2007 in China
By analysis of accidents’ characteristics, it can be concluded that the traffic accident rate increases under the water, snow, ice, and freezing road surface conditions and that road surface conditions greatly affect the highway traffic safety and transport efficiency
It is urgent to carry out research on the road surface state recognition and provide reference and theoretical basis for traffic control and meteorological management to ensure traffic safety [2]
Summary
16.12% of traffic accidents on the highway are ascribed to slippery road conditions [1] since 2007 in China. Chen [10] extracted low-order statistical features of road surface images including gray level cooccurrence matrix texture feature parameters and used linear discriminant function to determine the road surface state. Yamamoto et al [13] applied the human-computer interaction method to extract the gray scale value and temperature characteristics parameters of the road surface for the road surface state prediction, and measurement accuracy was tested to be more than 80%. Li et al [17] extracted RGB, HIS, and YUV of road surface images and established the road surface state recognition model based on improved BP neural network. (4) Extracting appropriate multidimensional color and texture eigenvectors can help to improve the accuracy of road surface state recognition. The algorithm proposed is tested and the ideal recognition results are obtained based on the large-scale samples
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