| 朱东涛,陈杰,杨星,邵慧,李钊.基于双通路跃层卷积网络的交通标志识别算法[J].安徽建筑大学学报,2018,26(1):61-66 |
| 基于双通路跃层卷积网络的交通标志识别算法 |
| A Traffic Sign Recognition Algorithm for Based on Double Channels Layer-Skipping Convolutional Neural Network |
| |
| DOI：10.11921/j.issn.2095-8382.20180112 |
| 中文关键词: 卷积神经网络 交通标志识别 双通路跃层 特征融合 深度学习 |
| 英文关键词: ： Convolutional Neural Network (CNN), Traffic Sign Recognition (TSR), double channels layer-skipping, feature fusion, deep learning |
| 基金项目:国家自然科学基金(61503394),安徽省教育厅重点项目(KJ2016ZD149)， 安徽省教育厅重大项目(KJ2015ZD14)。 |
|
| 摘要点击次数: 7819 |
| 全文下载次数: 0 |
| 中文摘要: |
| 交通标志识别(Traffic Sign Recognition, TSR)是智能交通系统的重要研究方向之一。因道路交通的环境复杂、交通标志数据库规模大小等因素制约，在设计TSR系统可行性方案时必须考虑算法的复杂度、识别率和鲁棒性。针对这一问题，本文提出了一种不同尺度的双通路跃层卷积神经网络算法，在同一通路上交通标志的底层局部特征和高层全局的特征，与不同通路上经过局部响应归一化和池化后的特征在全连接层融合，从而丰富了交通标志分类的特征，最后将特征图输入分类器进行交通标志识别。采用德国交通标志识别标准数据集（German Traffic Sign Recognition Benchmark, GTSRB）进行训练和测试，本文算法的识别率达到97.96%，明显优于单一通路的跃层卷积网络算法和人工方法。 |
| 英文摘要: |
| Traffic Sign Recognition (TSR) is one of the important directions in Intelligent Transportation System research. Restricted by the complex traffic environment, the traffic sign database scale and other factors, it is necessary to consider the complexity of the algorithm, recognition rate, and robustness. Concerning this issue, we propose Double Channels Layer-Skipping Convolutional Neural Network (DCLS-CNN) structure at different scales. The low-layer local features and high-layer global features of traffic signs on one channel, and other channel feature by the Local Response Normalization and pooling, which enriched the feature of traffic signs classification, these features fusion in the full connected layer, finally features were exported for traffic signs recognition. Tested by the German Traffic Sign Recognition Benchmark (GTSRB), the recognition rate of this algorithm is up to 97.96%, which is significantly higher than that of Layer-Skipping Convolutional Neural Network (LS-CNN) and hand-crafted methods. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
