[1]WANG Xiaotang,YAN He,LIU Jianqi,et al.A new deeplabv3+ semantic segmentation model of edge gradient interpolation with double branch structure[J].CAAI Transactions on Intelligent Systems,2023,18(3):604-612.[doi:10.11992/tis.202111023]
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A new deeplabv3+ semantic segmentation model of edge gradient interpolation with double branch structure

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 604-612 Column: 学术论文—知识工程 Public date: 2023-07-05
Title:
A new deeplabv3+ semantic segmentation model of edge gradient interpolation with double branch structure
Author(s):
WANG Xiaotang;  YAN He;  LIU Jianqi;  ZHANG Ye
School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China
Keywords:
edge gradient; image interpolation; bicubic interpolation; bilinear interpolation; deeplabv3+; dual branch structure; decoder; semantic segmentation
CLC:
TP391；TP181
DOI:
10.11992/tis.202111023
Abstract:
Aiming at the problem that the single branch structure of deeplabv3 + decoder with bilinear interpolation is easy to lead to the loss of high-frequency components of an image and the low accuracy of semantic segmentation, the Sobel operator is used to calculate the edge gradient values of each pixel along different directions, and by combination with bicubic interpolation algorithm, an edge gradient interpolation method is proposed. On this basis, the 1/8 input image and the encoder output are interpolated up to twice the edge gradient, then interpolated up to twice the edge gradient through feature fusion and edge gradient interpolation, and then interpolated up to four times the edge gradient after feature fusion with the 1/4 input image, thereby a double branch deeplabv3 +semantic segmentation model with edge gradient interpolation is established. The comparative experimental results show that the proposed method has improved MIOU indicator by 2.2% on the VOC2012 dataset compared with the original model, and has a better visual effect on image edge detail segmentation.
References:
[1] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[EB/OL]. (2014?12?22)[2021?11?12].https://arxiv.org/abs/1412.7062. 2014.
[2] 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, 2018, 40(4): 834–848.
[3] CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. (2017?06?17) [2021?11?12].https://arxiv.org/abs/1706.05587.
[4] CHEN L C, ZHU Yukun, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//European Conference on Computer Vision. Cham: Springer, 2018: 833?851.
[5] HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9): 1904–1916.
[6] 任凤雷, 何昕, 魏仲慧, 等. 基于DeepLabV3+与超像素优化的语义分割[J]. 光学精密工程, 2019, 27(12): 2722–2729
REN Fenglei, HE Xin, WEI Zhonghui, et al. Semantic segmentation based on DeepLabV3+ and superpixel optimization[J]. Optical precision engineering, 2019, 27(12): 2722–2729
[7] 吴涛, 董肖莉, 孟伟, 等. 基于语义分割的简洁线条肖像画生成方法[J]. 智能系统学报, 2021, 16(1): 134–141
WU Tao, DONG Xiaoli, MENG Wei, et al. Concise line portrait generation method based on semantic segmentation[J]. CAAI transactions on intelligent systems, 2021, 16(1): 134–141
[8] RUKUNDO O, CAO HANQIANG. Nearest neighbor value interpolation[EB/OL]. (2012?11?08)[2021?11?12].https://arxiv.org/abs/1211.1768.
[9] KIRKLAND E J. Bilinear interpolation[M]// Advanced Computing in Electron Microscopy. Boston: Springer, 2010: 261?263.
[10] HUANG Zhengzhong, CAO Liangcai. Bicubic interpolation and extrapolation iteration method for high resolution digital holographic reconstruction[J]. Optics and lasers in engineering, 2020, 130: 106090.
[11] RUKUNDO O. Effects of improved floor function on the accuracy of bilinear interpolation algorithm[EB/OL]. (2021?04?08)[2021?11?12].https://arxiv.org/abs/2104.03646.
[12] 王庆, 唐涛, 项德良, 等. 基于梯度比率的SAR图像局部特征提取方法研究[J]. 智能系统学报, 2017, 12(3): 286–292
WANG Qing, TANG Tao, XIANG Deliang, et al. Research on local feature extraction of SAR images based on gradient ratio[J]. CAAI transactions on intelligent systems, 2017, 12(3): 286–292
[13] 伍锡如, 凌星雨. 基于改进的Faster RCNN面部表情检测算法[J]. 智能系统学报, 2021, 16(2): 210–217
WU Xiru, LING Xingyu. Facial expression recognition based on improved Faster RCNN[J]. CAAI transactions on intelligent systems, 2021, 16(2): 210–217
[14] 徐聪, 王丽. 基于改进DeepLabv3+网络的图像语义分割方法[J]. 激光与光电子学进展, 2021, 58(16): 1610008
XU Cong, WANG Li. Image semantic segmentation method based on improved DeepPlabv 3+Network[J]. Laser & optoelectronics progress, 2021, 58(16): 1610008
[15] 王兴武, 雷涛, 王营博, 等. 基于多模态互补特征学习的遥感影像语义分割[J]. 智能系统学报, 2022, 17(6): 1123–1133
WANG Xingwu, LEI Tao, WANG Yingbo, et al. Semantic segmentation of remote sensing image based on multimodal complementary feature learning[J]. CAAI transactions on intelligent systems, 2022, 17(6): 1123–1133
[16] PANDA J, MEHER S. An efficient image interpolation using edge-error based sharpening[C]//2020 IEEE 17th India Council International Conference. New Delhi: IEEE, 2021: 1?6.
[17] VINCENT O, FOLORUNSO O. A descriptive algorithm for sobel image edge detection[C]//Proceedings of the 2009 InSITE Conference. Washington: Informing Science Institute. 2009, 40: 97?107.
[18] HOUBRECHTS K, DAS R, KEUPERS M, et al. A complete procedure to prepare virtual clinical trials in digital breast tomosynthesis[C]//Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging. San Diego: SPIE. 2023, 12463: 267?274.
[19] HUANG Zhengzhong, CAO Liangcai. Bicubic interpolation and extrapolation iteration method for high resolution digital holographic reconstruction[J]. Optics and lasers in engineering, 2020, 130: 106090.
[20] HORé A, ZIOU D. Image quality metrics: PSNR vs. SSIM[C]//2010 20th International Conference on Pattern Recognition. Istanbul: IEEE, 2010: 2366?2369.
[21] HAENDEL A, ANDERSON J G, PILZ M, et al. A frequency-dependent model for the shape of the Fourier amplitude spectrum of acceleration at high frequencies[J]. Bulletin of the seismological society of America, 2020, 110(6): 2743–2754.
[22] YU Lei, DENG Qiuyue, LIU Bin, et al. Dual-branch feature learning network for single image super-resolution[J]. Multimedia tools and applications, 2023: 1?18.
[23] EVERINGHAM M, ALI ESLAMI S M, VAN GOOL L, et al. The pascal visual object classes challenge: a retrospective[J]. International journal of computer vision, 2015, 111(1): 98–136.
[24] LI YUANZHI, WEI C, MA TENGYU. Towards explaining the regularization effect of initial large learning rate in training neural networks[EB/OL]. (2019?07?10)[2021?11?12].https://arxiv.org/abs/1907.04595.
[25] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2999?3007.
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