Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
清华大学学报(自然科学版)  2018, Vol. 58 Issue (8): 725-731    DOI: 10.16511/j.cnki.qhdxxb.2018.21.013
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
组合全卷积神经网络和条件随机场的道路分割
宋青松, 张超, 陈禹, 王兴莉, 杨小军
长安大学 信息工程学院, 西安 710064
Road segmentation using full convolutional neural networks with conditional random fields
SONG Qingsong, ZHANG Chao, CHEN Yu, WANG Xingli, YANG Xiaojun
School of Information Engineering, Chang'an University, Xi'an 710064, China
全文: PDF(2135 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 常见的道路分割方法往往环境噪声鲁棒性不足并且分割边缘不够平滑。针对该问题,提出了一种组合全卷积神经网络和全连接条件随机场的道路分割方法。首先,利用深度神经网络良好的特征表征能力,将道路分割视为一个二分类问题,构建一个基于VGG_16深度卷积网络的全卷积网络,实现道路图像端到端的路面和背景分类;然后,利用全连接条件随机场能够实现图像精细分割的特点,采用全连接条件随机场对二分类得到的粗糙边缘再进行平滑优化。针对真实环境下采集的道路分割基准数据库的测试结果表明:该方法获得了98.13%的分割准确率以及每0.84 s处理1幅图像的分割速度,具有一定的先进性。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
宋青松
张超
陈禹
王兴莉
杨小军
关键词 图像模式识别道路分割全卷积网络条件随机场    
Abstract:Common road segmentation methods are often limited by environmental noise and the roughness of the segmenting edges. A road segmentation method was developed to address these shortcomings by combining a fully convolutional neural network and a conditional random field. The feature representation in the neural networks models the road segmentation as a binary classification problem. A VGG-16 deep convolutional neural network based fully convolutional network was constructed to classify each road image end to end into the road and the background. Then, the fully-connected conditional random field (CRF) was used for fine segmentation to refine the coarse edges obtained from the binary classification. Tests of road segmentation benchmark datasets acquired in real environments show that this method can achieve 98.13% segmentation accuracy and real-time processing with 0.84 s perimage.
Key wordsimage pattern recognition    road segmentation    full convolutional neural network    conditional random field
收稿日期: 2018-01-24      出版日期: 2018-08-15
基金资助:国家自然科学基金资助项目(61201406,61473047);中央高校基本科研业务费专项资金资助项目(310824162022,300102248201,300102248401)
引用本文:   
宋青松, 张超, 陈禹, 王兴莉, 杨小军. 组合全卷积神经网络和条件随机场的道路分割[J]. 清华大学学报(自然科学版), 2018, 58(8): 725-731.
SONG Qingsong, ZHANG Chao, CHEN Yu, WANG Xingli, YANG Xiaojun. Road segmentation using full convolutional neural networks with conditional random fields. Journal of Tsinghua University(Science and Technology), 2018, 58(8): 725-731.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2018.21.013  或          http://jst.tsinghuajournals.com/CN/Y2018/V58/I8/725
  图1 VGG_16网络结构
  图2 组合全卷积网络和条件随机场的模型结构
  表1 全卷积网络训练损失和耗时
  图3 模型训练损失变化曲线
  图4 θ取值比选结果
  图6 部分图像的道路分割结果
  表2 三种组合模型测试准确率和耗时
  表3 平均分割准确率比较
[1] ALVAREZ J M, GEVERS T, LOPEZ A M. Road detection by one-class color classification:Dataset and experiments[R/OL]. (2014-12-18)[2017-10-20]. https://arxiv.org/abs/1412.3506.
[2] GRAOVAC S, GOMA A. Detection of road image borders based on texture classification[J]. International Journal of Advanced Robotic Systems, 2012, 9(242):1-12.
[3] WANG Y, SHEN D G, TEOH E K. Lane detection using spline model[J]. Pattern Recognition Letters, 2000, 21(8):677-689.
[4] WANG Y, TEOH E K, SHEN D G. Lane detection and tracking using B-Snake[J]. Image and Vision Computing, 2004, 22(4):269-280.
[5] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[6] NING F, DELHOMME D, LECUN Y, et al. Toward automatic phenotyping of developing embryos from videos[J]. IEEE Transactions on Image Processing, 2005, 14(9):1360-1371.
[7] GANIN Y, LEMPITSKY V. N4-fields:Neural network nearest neighbor fields for image transforms[C]//Proceedings of the 12th Asian Conference on Computer Vision. Singapore:Springer, 2014:536-551.
[8] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA:IEEE, 2015:3431-3440.
[9] LAFFERTY J D, MCCALLUM A, PEREIRA F C N. Conditional random fields:Probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the 18th International Conference on Machine Learning. San Francisco, USA:Morgan Kaufmann Publishers Inc., 2001:282-289.
[10] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//2015 International Conference on Learning Representations. Lille, France:University of Oxford, 2015:1-14.
[11] SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA:IEEE, 2015:1-9.
[12] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA:IEEE, 2016:770-778.
[13] ZEILER M D, KRISHNAN D, TAYLOR G W, et al. Deconvolutional networks[C]//2010 IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA:IEEE, 2010, 238(6):2528-2535.
[14] ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks[C]//The 13th European Conference on Computer Vision. Zurich, Switzerland:Springer, 2014:818-833.
[15] SHI W Z, CABALLERO J, HUSZÁR F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA:IEEE, 2016:1874-1883.
[16] Avisynth wiki. Resampling[R/OL]. (2016-09-22)[2017-11-12]. http://avisynth.nl/index.php/Resampling.
[17] KRAHENBUHL P, KOLTUN V. Efficient inference in fully connected crfs with Gaussian edge potentials[C]//The 24th International Conference on Neural Information Processing Systems. Granada, Spain:Neural Information Processing Systems Foundation, Inc., 2011:109-117.
[18] ZHENG S, JAYASUMANA S, ROMERA-PAREDES B, et al. Conditional random fields as recurrent neural networks[C]//2015 IEEE International Conference on Computer Vision, Santiago, Chile:IEEE, 2015:1529-1537.
[19] ÁLVAREZ J M, LÓPEZ A M, GEVERS T, et al. Combining priors, appearance, and context for road detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(3):1168-1178.
[20] The PASCAL visual object classes challenge 2012(VOC2012)[R/OL]. (2012-02-01)[2017-09-01]. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html.
[21] KINGMA D P, Ba J. Adam:A method for stochastic optimization[R/OL]. (2017-01-30)[2017-08-21]. https://arxiv.org/abs/1412.6980.
[1] 李煦, 屠明, 吴超, 国雁萌, 纳跃跃, 付强, 颜永红. 基于NMF和FCRF的单通道语音分离[J]. 清华大学学报(自然科学版), 2017, 57(1): 84-88.
[2] 刘泽文, 丁冬, 李春文. 基于条件随机场的中文短文本分词方法[J]. 清华大学学报(自然科学版), 2015, 55(8): 906-910,915.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn