基于卷积神经网络与集成学习的交通标志识别

Traffic Sign Recognition Based on Convolutional Neural Network and Ensemble Learning

为提高复杂情况(如遮挡、透视畸变等)下交通标志识别的精度,提出一种有效的基于卷积神经网络(Convolutional Neural Network,CNN)与集成学习的交通标志识别方法。首先通过融合颜色分割、形态学处理、形状检测等多种方法分割出交通标志,然后利用卷积神经网络对其特征进行提取并分别采用支持向量机(Support Vector Machine,SVM)和Softmax多类分类器对其进行识别,最后将2种分类结果进行集成作为最终的识别结果。实验结果表明,本文算法可有效提高复杂情况下交通标志识别精度,整体上具有较高的性能。

In order to improve the accuracy of traffic sign recognition under complex conditions(such as occlusion,perspective distortion,etc.),the paper presents an effective traffic sign recognition method based on convolutional neural network and ensemble learning.The proposed method firstly splits out the traffic signs by incorporating color segmentation,morphology processing and shape detection,and then identifies them using SVM and Softmax classifier based on the features extracted by CNN,respectively.Finally,the two kinds of classification results are integrated under the ensemble learning framework.Experimental results show that the proposed method can effectively improve the accuracy of traffic sign recognition under complex conditions,and has high overall performance.