【摘要】 针对交通标志识别识别率低和时间复杂度大的问题,本文提出一种HOGv-CLBP特征融合和极限学习机的交通标志识别算法。首先通过描述交通标志图像边缘信息的方向梯度直方图(HOG)特征,与能够表示标志图像内部纹理信息的局部二值模式(LBP)特征融合得到降维后形成一种HOGv-CLBP有效特征,然后利用ELM进行交通标志训练和分类。实验结果表明,该算法不仅提高了交通标志的识别率,而且降低了时间复杂度,增强了系统鲁棒性。更多还原

【Abstract】 In order to solve the problem of low recognition rate and time complexity of traffic sign recognition,this paper proposes an improved HOG feature extraction and extreme learning machine(ELM) traffic sign recognition algorithm.By describing the direction gradient histogram(HOG) feature of the edge information of the traffic sign image,and integrating with the local binary pattern(LBP) feature capable of representing the internal texture information of the logo image,a HOGv-LBP effective feature is formed and then utilized.ELM conducts traffic sign training and classification.The experimental results show that the proposed algorithm not only improves the recognition rate of traffic signs,but also reduces the time complexity and enhances the system robustness.更多还原