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- 2018
基于CNN多层特征和ELM的交通标志识别
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Abstract:
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。
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