计算机科学 ›› 2012, Vol. 39 ›› Issue (11): 226-229.

• 人工智能 • 上一篇    下一篇

ALPSO-SVM道路限速标志识别

王进,熊虎   

  1. (重庆邮电大学计算智能重庆市重点实验室 重庆400065)
  • 出版日期:2018-11-16 发布日期:2018-11-16

ALPO-SVM for Road Speed Limit Signs Recognition

  • Online:2018-11-16 Published:2018-11-16

摘要: 提出了一种基于支持向量机(Support Vcctor Machine, SVM)的道路限速标志识别方法。为了提高算法对限 速标志的识别精度,采用了一种可在进化过程中通过调整学习参数来协调粒子全局与局部搜索能力的自适应学习粒 子群算法(Adaptive Learning Particle Swarm Optimization, ALPSO)对支持向量机的相关参数进行优化。实验结果表 明,提出的ALPS(}SVM方法在识别性能上优于传统的SVM,在算法收敛性能上优于标准PS+SVM.

关键词: 限速标志识别,粒子群优化算法,支持向量机,参数优化

Abstract: This paper proposed a support vector machine (SVM)-based road speed limit signs recognition method. In order to improve the recognition performance of the algorithm, an adaptive learning particle swarm optimisation (ALP- SO) algorithm was employed to optimize the parameters of SV呱The proposed ALPSO may adjust its learning parame- ters during the evolutionary process to coordinate the local search with the global search of the PSO algorithm. Experi- mental results show that the proposed ALPSC}SVM is superior to the traditional SVM in terms of the recognition rate, and it's convergence performance is better than the standard PSC}SVM.

Key words: Speed limit signs recognition, Particle swarm optimization, Support vector machine, Parameters optimization

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