陕西师范大学学报（自然科学版）2017，Vol.45Issue(2)：24-28,5.DOI:10.15983/j.cnki.jsnu.2017.02.125
基于二级改进LeNet-5的交通标志识别算法
A traffic sign recognition algorithm based on the 2-level improved LeNet-5
摘要
Abstract
Focusing on GTSRB dataset acquired in real world, a traffic sign recognition algorithm based on the 2-level improved LeNet-5 is proposed,which combines convolutional neural networks with support vector machines.With the consideration of the requirement of real-time recognition, the traditional network structure of LeNet-5 is improved first.After GTSRB dataset images were cropped and converted to grayscale images, their brightness and size are normalized to 32×32 images.Next, a 2-level improved LeNet-5 is trained with GTSRB dataset, where the first level categorized traffic signs to 6 categories with the improved LeNet-5, and the second level improved LeNet-5 provide with the final category.Experimental results show that the proposed algorithm could provide with a correct recognition ratio 91.76%, since the multi-scale features could be fully analyzed with 2-level improved LeNet-5.关键词
卷积神经网络/交通标志/分类识别/支持向量机Key words
convolutional neural networks/traffic signs/classification/support vector machine分类
信息技术与安全科学引用本文复制引用
党倩,马苗,陈昱莅..基于二级改进LeNet-5的交通标志识别算法[J].陕西师范大学学报（自然科学版）,2017,45(2):24-28,5.基金项目
国家自然科学基金(61501287,61501286) （61501287,61501286）
陕西省重点实验室开放共享项目(SAIIP201202) （SAIIP201202）
陕西省自然科学基础研究计划(2015JQ6208) （2015JQ6208）
