Semantic segmentation based on DeepLabV3+ and superpixel optimization

Feng-lei REN; Xin HE; Zhong-hui WEI; You LV; Mu-yu LI

doi:10.3788/OPE.20192712.2722

您当前的位置：

首页 >

文章列表页 >

Semantic segmentation based on DeepLabV3+ and superpixel optimization

Information Sciences | 更新时间：2020-07-09

- Semantic segmentation based on DeepLabV3+ and superpixel optimization
- Optics and Precision Engineering Vol. 27, Issue 12, Pages: 2722-2729(2019)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192712.2722
  CLC： TP394.1
- Received：24 June 2019，
  
  Accepted：17 August 2019，
  
  Published：25 December 2019
- 稿件说明：
移动端阅览
Feng-lei REN, Xin HE, Zhong-hui WEI, et al. Semantic segmentation based on DeepLabV3+ and superpixel optimization[J]. Optics and precision engineering, 2019, 27(12): 2722-2729.
DOI：

Feng-lei REN, Xin HE, Zhong-hui WEI, et al. Semantic segmentation based on DeepLabV3+ and superpixel optimization[J]. Optics and precision engineering, 2019, 27(12): 2722-2729. DOI： 10.3788/OPE.20192712.2722.

摘要

针对基于深度学习的DeepLabV3+语义分割算法在编码特征提取阶段大量细节信息被丢失，导致其在物体边缘部分分割效果不佳的问题，本文提出了基于DeepLabV3+与超像素优化的语义分割算法。首先，使用DeepLabV3+模型提取图像语义特征并得到粗糙的语义分割结果；然后，使用SLIC超像素分割算法将输入图像分割成超像素图像；最后，融合高层抽象的语义特征和超像素的细节信息，得到边缘优化的语义分割结果。在PASCAL VOC 2O12数据集上的实验表明，相比较DeepLabV3+语义分割算法，本文算法在物体边缘等细节部分有着更好的语义分割性能，其mIoU值达到83.8%，性能得到显著提高并达到了目前领先的水平。

Abstract

To tackle the problem where by DeepLabV3+ loses considerable detail information during feature extraction

which leads to poor segmentation results in the edges of the objects

this study proposed a semantics segmentation algorithm based on DeepLabV3+ and optimized by superpixels. First

a DeepLabV3+ model was chosen to extract semantic features and obtain coarse semantic segmentation results. Then

the simple linear iterative clustering algorithm was used to segment the input image into superpixels. Finally

high-level abstract semantic features and detailed information of the superpixels were fused to obtain edge optimized semantic segmentation results. Experiments conducted on the PASCAL VOC 2O12 dataset show that compared to DeepLabV3+

the proposed algorithm had superior performance in terms of detail parts such as edges of objects

and the value of mIoU reached 83.8%.The proposed algorithm thus outperformed other state-of-the-art algorithms in terms of semantic segmentation.

关键词

Keywords

references

LADICKY L, SHI J, POLLEFEYS M.Pulling things out of perspective[C]. Proceedings of the IEEE conference on computer vision and pattern recognition , 2014: 89-96.

XIAO J, QUAN L.Multiple view semantic segmentation for street view images[C]. 2009 IEEE 12th international conference on computer vision , 2009: 686-693.

SHOTTON J, JOHNSON M, CIPOLLA R.Semantic texton forests for image categorization and segmentation[C]. 2008 IEEE Conference on Computer Vision and Pattern Recognition , 2008: 1-8.

TU Z, BAI X. Auto-context and its application to high-level vision tasks and 3d brain image segmentation[J]. IEEE transactions on pattern analysis and machine intelligence , 2009, 32(10):1744-1757.

FULKERSON B, VEDALDI A, SOATTO S.Class segmentation and object localization with superpixel neighborhoods[C]. 2009 IEEE 12th international conference on computer vision , 2009: 670-677.

KRIZHENVSHKY A, SUTSKEVER I, HINTON G.Imagenet classification with deep convolutional networks[C]. Proceedings of the Conference Neural Information Processing Systems (NIPS) , 1097-1105.

WU Z, SHEN C, VAN DEN HENGEL A. Wider or deeper: Revisiting the resnet model for visual recognition[J]. Pattern Recognition , 2019, 90:119-133.

李宇, 刘雪莹, 张洪群, 等.基于卷积神经网络的光学遥感图像检索[J].光学精密工程, 2018, 26(1):200-207.

LI Y, LIU X Y, ZHANG H Q, et al .. Optical remote sensing image retrieval based of convolutional neural networks[J]. Opt. Precision Eng. , 2018, 26(1):200-207. (in Chinese)

SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv: 1409.1556, 2014.

方明, 孙腾腾, 邵桢.基于改进YOLOv2的快速安全帽佩戴情况检测[J].光学精密工程, 2019, 27(5), 1196-1205.

FANG M, SUN T T, SHAO Z.Rapid helmet wear detection based on improved YOLOv2[J]. Opt. Precision Eng. , 2019, 27(5), 1196-1205. (in Chinese)

LONG J, SHELHAMER E, DARRELL T.Fully convolutional networks for semantic segmentation[C]. Proceedings of the IEEE conference on computer vision and pattern recognition , 2015: 3431-3440.

RONNEBERGER O, FISCHER P, BROX T.U-net: Convolutional networks for biomedical image segmentation[C]. International Conference on Medical image computing and computer-assisted intervention , 2015: 234-241.

BADRINARAYANAN V, KENDALL A, CIPOLLA R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE transactions on pattern analysis and machine intelligence , 2017, 39(12):2481-2495.

CHEN L-C, PAPANDREOU G, KOKKINOS I, et al.. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE transactions on pattern analysis and machine intelligence , 2017, 40(4):834-848.

CHEN L-C, PAPANDREOU G, SCHROFF F, et al.. Rethinking atrous convolution for semantic image segmentation[J]. arXiv preprint arXiv: 1706.05587, 2017.

CHEN L-C, ZHU Y, PAPANDREOU G, et al.. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]. Proceedings of the European Conference on Computer Vision (ECCV) , 2018: 801-818.

REN F, HE X, WER Z, et al.. Fusing appearance and prior cues for road detection[J]. Applied Sciences , 2019, 9(5):996.

WEI Z, YI F, WEI X, et al.. An improved image semantic segmentation method based on superpixels and conditional random fields[J]. Applied Science s, 2018, 8(5):837.

HE K, ZHANG X, REN S, et al.. Deep residual learning for image recognition[C]. Proceedings of the IEEE conference on computer vision and pattern recognition , 2016: 770-778.

ACHANTA R, SHAJI A, SMITH K, et al.. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence , 2012, 34(11):2274-2282.

LIU W, RABINOVICH A, BERG AC. Parsenet: Looking wider to see better[J]. arXiv preprint arXiv: 1506.04579, 2015.

EVERINGHAM M, VAN GOOL L, WILLIAMS CK, et al.. The pascal visual object classes (voc) challenge[J]. International journal of computer vision , 2010, 88(2):303-338.

HARIHARAN B, ARBELAEZ P, BOURDEV L, et al.. Semantic contours from inverse detectors[C]. 2011 International Conference on Computer Vision , 2011: 991-998.

Views

178

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Improved DeepLabv3+ semantic segmentation incorporating attention mechanisms

Real-time semantic segmentation based on improved BiSeNet

Real time semantic segmentation network of wire harness terminals based on multiple receptive field attention

Underwater image enhancement based on multi-branch residual attention network

Related Author

YAN He

LEI Qiuxia

WANG Xu

REN Fenglei

YANG Lu

ZHOU Haibo

ZHANG Shiyv

HE Xin

Related Institution

Liangjiang College of Artificial Intelligence， Chongqing University of Technology

Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control， School of Mechanical Engineering， Tianjin University of Technology

National Demonstration Center for Experimental Mechanical and Electrical Engineering Education，Tianjin University of Technology

Changchun Institute of Optics，Fine Mechanics and Physics，Chinese Academy of Sciences

Transcend Communication Technology Tianjin Co.， Ltd

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰