基于卷积神经网络的交通场景语义分割方法研究

doi:10.11959/j.issn.1000-436x.2018053

Abstract

Abstract:

In order to improve the semantic segmentation accuracy of traffic scene,a segmentation method was proposed based on RGB-D image and convolutional neural network.Firstly,on the basis of semi-global stereo matching algorithm,the disparity map was obtained,and the sample library was established by fusing the disparity map D and RGB image into the four-channel RGB-D image.Then,with two different structures,the networks were trained by using two different learning rate adjustment strategy respectively.Finally,the traffic scene semantic segmentation test was carried out with RGB-D image as the input,and the results were compared with the segmentation method based on RGB image.The experimental results show that the proposed traffic scene segmentation algorithm based on RGB-D image can achieve higher semantic segmentation accuracy than that based on RGB image.

Key words: deep learning, convolutional neural network, traffic scene, semantic segmentation, disparity map

CLC Number:

U495，TP391.4

Linhui LI,Bo QIAN,Jing LIAN,Weina ZHENG,Yafu ZHOU. Study on traffic scene semantic segmentation method based on convolutional neural network[J]. Journal on Communications, 2018, 39(4): 123-130.

Figures/Tables 6

References 25

[1]	ANBALAGAN T , GOWRISHANKAR C , SHANMUGAM A . SVM based road surface detection to improve performance of ABS[J]. Journal of Theoretical ＆ Applied Information Technology, 2013,51(2): 234-239.
[2]	LECUN Y , BENGIO Y , HINTON G . Deep learning[J]. Nature, 2015,521(7553): 436-444.
[3]	高常鑫, 桑农 . 基于深度学习的高分辨率遥感影像目标检测[J]. 测绘通报, 2014(S1): 108-111.
	GAO C X , SANG N . Deep learning for object detection in remote sensing image[J]. Bulletin of Surveying and Mapping, 2014(S1): 108-111.
[4]	高凯珺, 孙韶媛, 姚广顺 ,等. 基于深度学习的无人车夜视图像语义分割[J]. 应用光学, 2017,38(3): 421-428.
	GAO K J , SUN S Y , YAO G S ,et al. Semantic segmentation of night vision images for unmanned vehicles based on deep learning[J]. Journal of Applied Optics, 2017,38(3): 421-428.
[5]	刘丹, 刘学军, 王美珍 . 一种多尺度 CNN 的图像语义分割算法[J]. 遥感信息, 2017,32(1): 57-64.
	LIU D , LIU X J , WANG M Z . Semantic segmentation with multi-scale convolutional neural network[J]. Remote Sensing Information, 2017,32(1): 57-64.
[6]	KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks[J]. Advances in Neural Information Processing Systems, 2012,25(2): 1-9.
[7]	SIMONYAN K , ZISSERMAN A . Very deep convolutional networks for large-scale image recognition[J]. ArXiv Preprint,ArXiv:1409.1556, 2014.
[8]	SZEGEDY C , LIU W , JIA Y ,et al. Going deeper with convolutions[C]// IEEE Conference on Computer Vision and Pattern Recognition. 2014: 1-9.
[9]	DENG J , DONG W , SOCHER R ,et al. ImageNet:a large-scale hierarchical image database[C]// IEEE Computer Vision and Pattern Recognition. 2009: 248-255.
[10]	LONG J , SHELHAMER E , DARRELL T . Fully convolutional networks for semantic segmentation[C]// IEEE Computer Vision and Pattern Recognition. 2015: 3431-3440.
[11]	BADRINARAYANAN V , HANDA A , CIPOLLA R . SegNet:a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling[J]. ArXiv Preprint,ArXiv:1505.07293, 2015.
[12]	BADRINARAYANAN V , KENDALL A , CIPOLLA R . SegNet:a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis ＆ Machine Intelligence, 2017,PP(99): 1.
[13]	NOH H , HONG S , HAN B . Learning deconvolution network for semantic segmentation[C]// IEEE International Conference on Computer Vision. 2015: 1520-1528.
[14]	SILBERMAN N , HOIEM D , KOHLI P ,et al. Indoor segmentation and support inference from RGBD images[C]// European Conference on Computer Vision. 2012: 746-760.
[15]	GUPTA S , GIRSHICK R , ARBELáEZ P ,et al. Learning rich features from RGB-D images for object detection and segmentation[C]// European Conference on Computer Vision. 2014: 345-360.
[16]	SHAO T , XU W , ZHOU K ,et al. An interactive approach to semantic modeling of indoor scenes with an RGBD camera[J]. ACM Transactions on Graphics, 2012,31(6): 439-445.
[17]	FILLIAT D , BATTESTI E , BAZEILLE S ,et al. RGBD object recognition and visual texture classification for indoor semantic mapping[C]// 2012 IEEE International Conference on Technologies for Practical Robot Applications. 2012: 127-132.
[18]	GEIGER A , LENZ P , URTASUN R . Are we ready for autonomous driving? The KITTI vision benchmark suite[C]// IEEE Conference on Computer Vision and Pattern Recognition. 2012: 3354-3361.
[19]	LI L , HUANG H , QIAN B ,et al. Vehicle detection method based on mean shift clustering[J]. Journal of Intelligent ＆ Fuzzy Systems, 2016,31(3): 1355-1363.
[20]	MIN D , CHOI S , LU J ,et al. Fast global image smoothing based on weighted least squares[J]. IEEE Transactions on Image Processing a Publication of the IEEE Signal Processing Society, 2014,23(12): 5638-5653.
[21]	RUSSELL B C , TORRALBA A , MURPHY K P ,et al. LabelMe:a database and web-based tool for image annotation[J]. International Journal of Computer Vision, 2008,77(1-3): 157-173.
[22]	GOULD S , FULTON R , KOLLER D . Decomposing a scene into geometric and semantically consistent regions[C]// IEEE International Conference on Computer Vision. 2009: 1-8.
[23]	EIGEN D , FERGUS R . Predicting depth,surface normals and semantic labels with a common multi-scale convolutional architecture[C]// IEEE International Conference on Computer Vision. 2015: 2650-2658.
[24]	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.
[25]	JIA Y , SHELHAMER E , DONAHUE J ,et al. Caffe:convolutional architecture for fast feature embedding[C]// The 22nd ACM International Conference on Multimedia. 2014: 675-678.

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网络模型	图像格式				分割精度				平均精确率	全局精确率
网络模型	图像格式	天空	建筑	路面	人行道	树木	草坪	车辆	平均精确率	全局精确率
SegNet	RGB	0.952	0.744	0.964	0.658	0.877	0.562	0.880	0.805	0.858
	RGB-D	0.964	0.778	0.969	0.661	0.869	0.597	0.911	0.821	0.875
SegNet-Basic	RGB	0.941	0.815	0.946	0.677	0.815	0.630	0.856	0.811	0.859
	RGB-D	0.937	0.866	0.956	0.768	0.792	0.709	0.892	0.846	0.870

网络模型	图像格式				分割精度				平均精确率	全局精确率
网络模型	图像格式	天空	建筑	路面	人行道	树木	草坪	车辆	平均精确率	全局精确率
SegNet	RGB	0.946	0.779	0.941	0.652	0.830	0.533	0.844	0.789	0.842
	RGB-D	0.949	0.809	0.944	0.677	0.812	0.522	0.877	0.799	0.852
SegNet-Basic	RGB	0.943	0.783	0.902	0.700	0.818	0.735	0.865	0.821	0.840
	RGB-D	0.936	0.789	0.915	0.757	0.830	0.718	0.866	0.830	0.855

Study on traffic scene semantic segmentation method based on convolutional neural network

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