[1]TAN Ruijun,ZHAO Zhicheng,XIE Xinlin.Double-residual semantic segmentation network and traffic scenic application[J].CAAI Transactions on Intelligent Systems,2022,17(4):780-787.[doi:10.11992/tis.202106020]
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Double-residual semantic segmentation network and traffic scenic application

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 4 Page number: 780-787 Column: 学术论文—人工智能基础 Public date: 2022-07-05
Title:
Double-residual semantic segmentation network and traffic scenic application
Author(s):
TAN Ruijun;  ZHAO Zhicheng;  XIE Xinlin
School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
Keywords:
double-residual network; detail feature extraction; jump feature fusion; up sampling; network branch training; semantic image segmentation; CamVid data set; Cityscapes data set
CLC:
TP183
DOI:
10.11992/tis.202106020
Abstract:
An image semantic segmentation network based on the double-residual network, named DRsenet, is proposed to address the depth problem of feature extraction network in semantic image segmentation and resolution reduction of feature maps by a down-sampling pooling layer. Firstly, a double-residual network is proposed to extract the detailed features of each target in the training set and improve the perception ability of the network for some detailed targets. Secondly, the jump feature fusion starts from Layer 1, and the up-sampling operation is continued by the 2×deconvolution method to fuse the low-level and the high-level features to reduce the impact of partial detail information loss on the segmentation accuracy. Finally, the network branch training method is adopted to train the outline features of each target on the image, followed by training the detailed features of each target. The results indicate that the network’s PMIOU improves from 49.72% to 59.44% on CamVid and from 44.35% to 47.77% on Cityscapes when compared to the full convolution network. The network can produce image segmentation results with higher accuracy and more complete edge segmentation.
References:
[1] 朱磊, 滕奇志, 龚剑. 基于改进模糊C均值聚类和区域合并的矿物颗粒分割方法[J]. 科学技术与工程, 2020, 20(34): 14138–14145
ZHU Lei, TENG Qizhi, GONG Jian. Mineral particle segmentation algorithm based on improved fuzzy C-means and region merging[J]. Science technology and engineering, 2020, 20(34): 14138–14145
[2] 许林, 孟娜, 袁静, 等. 基于边缘检测和图像分割的超声诊断机器人控制系统设计[J]. 计算机测量与控制, 2020, 28(8): 125–129
XU Lin, MENG Na, YUAN Jing, et al. Design of ultrasonic diagnosis robot control system based on edge detection and image segmentation[J]. Computer measurement & control, 2020, 28(8): 125–129
[3] 陈飞. 改进的交互式Otsu红外图像分割算法[J]. 计算机测量与控制, 2020, 28(9): 248–251
CHEN Fei. An improved interactive otsu infrared image segmentation algorithm[J]. Computer measurement & control, 2020, 28(9): 248–251
[4] 杨金鑫, 杨辉华, 李灵巧, 等. 结合卷积神经网络和超像素聚类的细胞图像分割方法[J]. 计算机应用研究, 2018, 35(5): 1569–1572,1577
YANG Jinxin, YANG Huihua, LI Lingqiao, et al. Cell image segmentation method based on convolution neural network and super pixel clustering[J]. Application research of computers, 2018, 35(5): 1569–1572,1577
[5] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the acm, 2017, 60(6): 84–90.
[6] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//3rd International Conference on Learning Representations, ICLR 2015-Conference Track Proceedings. San Diego: [s. n. ], 2015.
[7] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 1?9.
[8] TRONG V H, HYUN Y G, YOUNG K J, et al. Yielding multi-fold training strategy for image classification of imbalanced weeds[J]. Applied sciences, 2021, 11(8): 3331.
[9] MüLLER D, KRAMER F. MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning[EB/OL]. (2019?10?21)[2021?06?13].https://www.semanticscholar.org/paper/MIScnn%3A-A-Framework-for-Medical-Image-Segmentation-M%C3%BCller-Kramer.
[10] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[C]//IEEE transactions on pattern analysis and machine intelligence. New York: IEEE, 2015 : 640?651.
[11] EVERINGHAM M, ESLAMI S M A, GOOL L, et al. The pascal visual object classes challenge: a retrospective[J]. International journal of computer vision, 2015, 111(1): 98–136.
[12] SILBERMAN N, HOIEM D, KOHLI P, et al. Indoor segmentation and support inference from RGBD images[C]//European Conference on Computer Vision. Berlin: Springer, 2012: 746?760.
[13] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[M]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2015: 234?241.
[14] XIAO Zhitao, LIU Bowen, GENG Lei, et al. Segmentation of lung nodules using improved 3D-UNet neural network[J]. Symmetry, 2020, 12(11): 1787.
[15] GUAN Haixing, LI Hongliang, LI Rongqiang, et al. Face detection of innovation base based on faster RCNN[M]//2021 International Conference on Applications and Techniques in Cyber Intelligence. Cham: Springer International Publishing, 2021: 158?165.
[16] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1026?1034.
[17] MANDAL R, AZAM B, VERMA B, et al. Deep learning model with GA-based visual feature selection and context integration[C]//2021 IEEE Congress on Evolutionary Computation. Kraków: IEEE, 2021: 288?295.
[18] YANG Tao, WU Yan, ZHAO Junqiao, et al. Semantic segmentation via highly fused convolutional network with multiple soft cost functions[J]. Cognitive systems research, 2019, 53: 20–30.
[19] JIA Fan, LIU Jun, TAI Xuecheng. A regularized convolutional neural network for semantic image segmentation[J]. Analysis and applications, 2021, 19(1): 147–165.
[20] CORDTS M, OMRAN M, RAMOS S, et al. The cityscapes dataset for semantic urban scene understanding[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 3213?3223.
[21] KAZEROUNI I A, DOOLY G, TOAL D. Ghost-UNet: an asymmetric encoder-decoder architecture for semantic segmentation from scratch[J]. IEEE access, 2021, 9: 97457–97465.
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