针对传统神经网络仅利用端层特征进行分类导致特征不全面,以及交通标志识别中计算量大、时间长等问题,提出基于多层特征表达和极限学习机的交通标志识别方法.利用CNN网络提取多层交通标志特征图;采用多尺度池化操作,将提取出的各层特征向量联合形成一个具有多尺度多属性特征的交通标志特征向量;使用极限学习机分类器准确快速地实现交通标志的识别.实验结果表明,该方法能有效地提高交通标志识别的准确率,且具有较好的泛化能力和实时性.%The traditional neural network only uses the end-layer feature and needs massive and time-consuming computation in the traffic sign recognition, thereby resulting in an inaccurate and non-real-time classification. To solve the problem, a traffic sign recognition (TSR) method based on multi-layer feature expression and extreme learning machine (ELM) is proposed. Firstly, the multi-layer features of traffic signs are extracted using the convolutional neural network (CNN). Then, the multi-scale pooling operation is used to combine the extracted feature vectors of each layer to form a multi-scale multi-attribute traffic sign feature vector. Finally, the extreme learning machine (ELM) classifier is used to realize the classification of traffic signs. Experimental results show that the proposed method can effectively improve the accuracy and it has strong generalization ability and real-time performance in TSR.
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