Application of PSO-SVM Theory in Pavement Recognition

doi:10.3969/j.issn.1006-2475.2016.09.025

Abstract

Abstract: The road has a serious impact on the speed of the car, and affects the road traffic safety to some extent. In the identification and detection of pavement, the noise points and outliers are difficult problems in the identification and detection of pavement data. In this paper, with the support vector machine for road noise in the data points and outliers being of sensitive features, we present an improved PSO-SVM recognition algorithm. The algorithm firstly uses parameter to optimize hyperplane equation, then optimizes SVM kernel functions and their parameters by using Particle Swarm Optimization (PSO) algorithms, and finally recognizes and detects the pavement. The experimental results show that the proposed algorithm has the advantages of faster speed for the detection and calculation of pavement fluctuation, and high recognition accuracy which can reach up to 92%.

Key words: SVM theory, PSO algorithm, pavement recognition

CLC Number:

TP319

YAO Qing-hua, PU Wen. Application of PSO-SVM Theory in Pavement Recognition[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.09.025.

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