| 基于改进卷积神经网络的交通标志识别方法 |
| |
| 引用本文： | 袁小平,王岗,王晔枫,汪喆远,孙辉. 基于改进卷积神经网络的交通标志识别方法[J]. 电子科技, 2019, 32(11): 28-32. DOI: 10.16180/j.cnki.issn1007-7820.2019.11.006 |
| |
| 作者姓名： | 袁小平 王岗 王晔枫 汪喆远 孙辉 |
| |
| 作者单位： | 中国矿业大学 信息与控制工程学院,江苏 徐州221000 |
| |
| 基金项目： | 江苏省自然科学基金(BK20170278) |
| |
| 摘 要： | 针对智能交通系统中小尺度交通标志识别率低的问题,文中提出一种改进卷积神经网络的交通标志识别方法。该方法通过在Faster R-CNN算法的低层特征图上增加优化的RPN网络,提升了小尺度交通标志的检测率。该方法还利用Max Pooling方法实了现图像的局部细节特征与全局语义特征充分融合。在TT-100K数据集上稍微实验结果表明新方法可以明显提高小尺度交通标志的识别率。
|
| 关 键 词： | 深度学习 交通标志识别 卷积神经网络 Faster R-CNN RPN 特征融合 |
| 收稿时间： | 2018-11-17 |
Traffic Sign Recognition Method Based on Improved Convolutional Neural Network |
| |
| YUAN Xiaoping,WANG Gang,WANG Yefeng,WANG Zheyuan,SUN Hui. Traffic Sign Recognition Method Based on Improved Convolutional Neural Network[J]. Electronic Science and Technology, 2019, 32(11): 28-32. DOI: 10.16180/j.cnki.issn1007-7820.2019.11.006 |
| |
| Authors: | YUAN Xiaoping WANG Gang WANG Yefeng WANG Zheyuan SUN Hui |
| |
| Affiliation: | School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221000,China |
| |
| Abstract: | Aiming at the low recognition rate of small scale traffic signs in intelligent transportation system, an improved convolution neural network method for traffic sign recognition was proposed in this paper.This method could improve the detection rate of small-scale traffic signs by adding an optimized RPN network to the low-level feature map of Faster R-CNN algorithm.In addition, Max Pooling method was used to fully fuse the local details and global semantic features of the image. The experimental results on TT-100K data set showed that the proposed method could significantly improve the recognition rate of small-scale traffic signs. |
| |
| Keywords: | deep learning trafficsign recognition convolutional neural network Faster R-CNN RPN feature fusion |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《电子科技》浏览原始摘要信息 |
|
点击此处可从《电子科技》下载全文 |
