基于DenseNet的复杂交通场景语义分割方法

doi:10.16451/j.cnki.issn1003-6059.201905010

Abstract
Figure/Table
References
Related Citation (2)

Download: PDF (2007 KB) HTML (0 KB)
Export: BibTeX | EndNote (RIS)

Abstract An end-to-end multi-scale semantic segmentation model based on fully convolutional DenseNet is proposed, aiming at the problems of traditional semantic segmentation methods for street scene, such as the large number of parameters and low computational efficiency and precision. Firstly, convolution layers embedded with hybrid dilation convolution are stacked to establish a dense module, and then the modules are cascaded along channel dimension to extract features. Next, multi-scale visual information regarded as supervised signals are transferred back to original channels. Finally, the prediction results are obtained by bilinear interpolation method. Experimental results on Cityscapes dataset demonstrate that the proposed method achieves an efficient segmentation and performs a better accuracy for street scene parsing.

Key words： Traffic Scene Image Semantic Segmentation Dilated Convolution Multi-scale Feature Fusion

Received: 15 September 2018

ZTFLH:

TP 391

Fund:Supported by Young Scientists Fund of National Natural Science Foundation of China(No.61702176), Natural Science Foundation of Hunan Province(No.2017JJ3038)

Corresponding Authors: (JIANG Bin(Corresponding author), Ph.D., associate professor. His research interests include big data technology, computer vision and machine learning.)

About author:: (TU Wenxuan, master student. His research interests include computer vision and machine learning.)(YANG Chao, Ph.D., associate professor. Her research interests include big data technology, social network computing and intelligent information processing.)(LIU Hongyu, master student. His research interests include computer vision and machine learning.)(ZHAO Zilong, master student. His research interests include computer vision and machine learning.)

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	JIANG Bin¹
	TU Wenxuan¹
	YANG Chao¹
	LIU Hongyu¹
	ZHAO Zilong¹

Cite this article:

JIANG Bin¹,TU Wenxuan¹,YANG Chao¹等. Semantic Segmentation Method for Complex Traffic Scene Based on DenseNet[J]. , 2019, 32(5): 472-480.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201905010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I5/472

[1] GARCIA-GARCIA A, ORTS-ESCOLANO S, OPREA S, et al. A Review on Deep Learning Techniques Applied to Semantic Segmentation[C/OL]. [2018-08-15]. https://arxiv.org/pdf/1704.06857.pdf.
[2] 张新明,祝晓斌,蔡强,等.图像语义分割深度学习模型综述.高技术通讯, 2017, 27(9): 808-815.
(ZHANG X M, ZHU X B, CAI Q, et al. Survey of the Deep Learning Models for Image Semantic Segmentation. Chinese High Technology Letters, 2017, 27(9): 808-815.)
[3] 姜枫,顾庆,郝慧珍,等.基于内容的图像分割方法综述.软件学报, 2017, 28(1): 160-183.
(JIANG F, GU Q, HAO H Z, et al. Survey on Content-Based Image Segmentation Methods. Journal of Software, 2017, 28(1): 160-183.)
[4] ZHAO H S, QI X J, SHEN X Y, et al. ICNet for Real Time Semantic Segmentation on High-Resolution Images[C/OL]. [2018-08-15]. https://arxiv.org/pdf/1704.08545.pdf.
[5] 李琳辉,钱波,连静,等.基于卷积神经网络的交通场景语义分割方法研究.通信学报, 2018, 39(4): 123-130.
(LI L H, QIAN B, LIAN J, et al. Study on Traffic Scene Semantic Segmentation Method Based on Convolutional Neural Network. Journal on Communications, 2018, 39(4): 123-130.)
[6] LIU C, YUEN J, TORRALBA A. Sift Flow: Dense Corresponden-ce across Scenes and Its Applications // HASSNER T, LIU C, eds. Dense Image Correspondences for Computer Vision. Berlin, Germany: Springer, 2011: 15-49.
[7] FARABET C, COUPRIE C, NAJMAN L, et al. Learning Hierarchical Features for Scene Labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1915-1929.
[8] LONG J, SHELHAMER E, DARRELL T. Fully Convolutional Networks for Semantic Segmentation[C/OL]. [2018-08-15]. https://arxiv.org/pdf/1411.4038.pdf.
[9] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 2017, 60(6): 84-90.
[10] SIMONYAN K, ZISSERMAN A. Very Deep Convolutional Networks for Large-Scale Image Recognition[C/OL]. [2018-08-15]. https://arxiv.org/pdf/1409.1556.pdf.
[11] SZEGEDY C, LIU W, JIA Y Q, et al. Going Deeper with Convolutions // Proc of the 28th IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 1-9.
[12] HE K M, ZHANG X Y, REN S Q, et al. Deep Residual Learning for Image Recognition // Proc of the 28th IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2015: 770-778.
[13] BADRINARAYANAN V, HANDA A, CIPOLLA R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2015, 39(12): 2481-2495.
[14] RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional Networks for Biomedical Image Segmentation // Proc of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2015: 234-241.
[15] BANSAL A, CHEN X L, RUSSELL B, et al. PixelNet: Representation of the Pixels, by the Pixels, and for the Pixels[C/OL]. [2018-08-15]. https://arxiv.org/pdf/1702.06506.pdf.
[16] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs[C/OL]. [2018-08-15]. https://arxiv.org/pdf/1412.7062.pdf.
[17] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.
[18] ZHAO H S, SHI J P, QI X J, et al. Pyramid Scene Parsing Network // Proc of the IEEE Conference on Computer Vision and Pa-ttern Recognition. Washington, USA: IEEE, 2017: 6230-6239.
[19] YU F, KOLTUN V. Multi-scale Context Aggregation by Dilated Convolutions[C/OL]. [2018-08-15]. https://arxiv.org/pdf/1511.07122.pdf.
[20] YU F, KOLTUN V, FUNKHOUSER T. Dilated Residual Networks // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 636-644.
[21] CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking Atrous Convolution for Semantic Image Segmentation[C/OL]. [2018-08-15]. https://arxiv.org/pdf/1706.05587.pdf.
[22] XIE C W, ZHOU H Y, WU J X. Vortex Pooling: Improving Con-text Representation in Semantic Segmentation[C/OL]. [2018-08-15]. https://arxiv.org/pdf/1804.06242v2.pdf.
[23] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely Connected Convolutional Networks // Proc of the IEEE Conference on
Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2017: 2261-2269.
[24] DENG J, DONG W, SOCHER R, et al. ImageNet: A Large-Scale Hierarchical Image Database // Proc of the 22th IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2009: 248-255.
[25] BENENSON R, FRANKE U, ROTH S, et al. The Cityscapes Da-taset for Semantic Urban Scene Understanding // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 3213-3223.