SPSSNet：一种用于图像语义分割的实时网络

Saqib MAMOON; Muhammad Arslan MANZOOR; 张发恩; Zakir ALI; 陆建峰

doi:10.1631/FITEE.1900697

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SPSSNet：一种用于图像语义分割的实时网络

常规文章 | Updated：2022-05-19

- SPSSNet：一种用于图像语义分割的实时网络
- SPSSNet: a real-time network for image semantic segmentation
- 信息与电子工程前沿（英文） 2020年21卷第12期页码：1770-1782
- Affiliations：
  
  School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing 210094, China
  AInnovation, Beijing 100080, China
- Author bio：
  
  [ "Saqib MAMOON, E-mail: saqibmamoon@njust.edu.cn" ]
  [ "Muhammad Arslan MANZOOR, E-mail: arsalaan@njust.edu.cn" ]
  [ "Fa-en ZHANG, E-mail: zhangfaen@ainnovation.com" ]
  [ "Zakir ALI, E-mail: alizakir@njust.edu.cn" ]
  Jian-feng LU, E-mail: lujf@njust.edu.cn
- Funds：
  
  Project supported by the National Key R&D Program of China (No. 2017YFB1300205)
- DOI：10.1631/FITEE.1900697
  中图分类号： TP39
- 收稿：2019-12-14，
  
  修回：2020-;7-22，
  
  纸质出版：2020-12
- Accepted：
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Saqib MAMOON, Muhammad Arslan MANZOOR, 张发恩, 等. SPSSNet：一种用于图像语义分割的实时网络[J]. 信息与电子工程前沿（英文）, 2020,21(12):1770-1782.

Saqib MAMOON, Muhammad Arslan MANZOOR, Fa-en ZHANG, et al. SPSSNet: a real-time network for image semantic segmentation[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1770-1782.
Saqib MAMOON, Muhammad Arslan MANZOOR, 张发恩, 等. SPSSNet：一种用于图像语义分割的实时网络[J]. 信息与电子工程前沿（英文）, 2020,21(12):1770-1782. DOI： 10.1631/FITEE.1900697.

Saqib MAMOON, Muhammad Arslan MANZOOR, Fa-en ZHANG, et al. SPSSNet: a real-time network for image semantic segmentation[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(12): 1770-1782. DOI： 10.1631/FITEE.1900697.

摘要

深度神经网络（DNNs）虽已在语义分割领域取得极大成功，但要实现实时推理仍然是一项巨大挑战。大量特征通道、参数与浮点运算极大延缓了网络的推理速度，导致无法满足诸如机器人控制、自动驾驶等实时任务要求。现有大多数方法是通过牺牲空间分辨率来加速推理，往往导致推理结果准确率下降。针对此问题，提出一种新的轻量级阶段池化语义分割网络（SPSSN）。该网络可以保留浅层学习得到的重要特征并在后续层中重复使用。SPSSN以2048×1024的全分辨率图像作为输入，网络模型仅包含1.42×10

参数。在无预训练情况下，在Cityscapes数据集上可达到69.4%的mIoU精度，推理速度则可达到每秒59帧。由于SPSSN结构轻巧，它可以在移动设备上实时运行。最后，为验证本文方法有效性，与当前最优网络进行了对比。

Abstract

Although deep neural networks (DNNs) have achieved great success in semantic segmentation tasks

it is still challenging for real-time applications. A large number of feature channels

parameters

and floating-point operations make the network sluggish and computationally heavy

which is not desirable for real-time tasks such as robotics and autonomous driving. Most approaches

however

usually sacrifice spatial resolution to achieve inference speed in real time

resulting in poor performance. In this paper

we propose a light-weight stage-pooling semantic segmentation network (SPSSN)

which can efficiently reuse the paramount features from early layers at multiple stages

at different spatial resolutions. SPSSN takes input of full resolution 2048

$$\times$$

1024 pixels

uses only 1.42

$$\times 10^6$$

parameters

yields 69.4% mIoU accuracy without pre-training

and obtains an inference speed of 59 frames/s on the Cityscapes dataset. SPSSN can run directly on mobile devices in real time

due to its light-weight architecture. To demonstrate the effectiveness of the proposed network

we compare our results with those of state-of-the-art networks.

关键词

Keywords

references

V Badrinarayanan , , , A Kendall , , , R Cipolla . . SegNet: a deep convolutional encoder-decoder architecture for image segmentation . . IEEE Trans Patt Anal Mach Intell , , 2017 . . 39 ( ( 12 ): ): 2481 - - 2495 . . DOI: 10.1109/TPAMI.2016.2644615 http://doi.org/10.1109/TPAMI.2016.2644615 . .

GJ Brostow , , , J Fauqueur , , , R Cipolla . . Semantic object classes in video: a high-definition ground truth database . . Patt Recogn Lett , , 2009 . . 30 ( ( 2 ): ): 88 - - 97 . . DOI: 10.1016/j.patrec.2008.04.005 http://doi.org/10.1016/j.patrec.2008.04.005 . .

LC Chen , , , G Papandreou , , , F Schroff , , , 等 . . Rethinking atrous convolution for semantic image segmentation . . 2017 . . https://arxiv.org/abs/1706.05587 https://arxiv.org/abs/1706.05587 , , . .

LC Chen , , , G Papandreou , , , I Kokkinos , , , 等 . . DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs . . IEEE Trans Patt Anal Mach Intell , , 2018 . . 40 ( ( 4 ): ): 834 - - 848 . . DOI: 10.1109/TPAMI.2017.2699184 http://doi.org/10.1109/TPAMI.2017.2699184 . .

J Cheng , , , P Wang , , , G Li , , , 等 . . Recent advances in efficient computation of deep convolutional neural networks . . Front Inform Technol Electron Eng , , 2018 . . 19 ( ( 1 ): ): 64 - - 77 . . DOI: 10.1631/FITEE.1700789 http://doi.org/10.1631/FITEE.1700789 . .

F Chollet . . Xception: deep learning with depthwise separable convolutions . . 2016 . . https://arxiv.org/abs/1610.02357 https://arxiv.org/abs/1610.02357 , , . .

PF Christ , , , MEA Elshaer , , , F Ettlinger , , , 等 . . Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields . . Proc $19^{\rm th}$ Int Conf on Medical Image Computing and Computer-Assisted Intervention , , 2016 . . p.415 - - 423 . . DOI: 10.1007/978-3-319-46723-8_48 http://doi.org/10.1007/978-3-319-46723-8_48 . .

M Cordts , , , M Omran , , , S Ramos , , , 等 . . The Cityscapes dataset for semantic urban scene understanding . . Proc IEEE Conf on Computer Vision and Pattern Recognition , , 2016 . . p.3213 - - 3223 . . DOI: 10.1109/CVPR.2016.350 http://doi.org/10.1109/CVPR.2016.350 . .

JF Dai , , , KM He , , , Y Li , , , 等 . . Instance-sensitive fully convolutional networks . . Proc $14^{\rm th}$ European Conf on Computer Vision , , 2016a . . p.534 - - 549 . . DOI: 10.1007/978-3-319-46466-4_32 http://doi.org/10.1007/978-3-319-46466-4_32 . .

JF Dai , , , Y Li , , , KM He , , , 等 . . R-FCN: object detection via region-based fully convolutional networks . . Proc $30^{\rm th}$ Int Conf on Neural Information Processing Systems , , 2016b . . p.379 - - 387 . . . .

J Devlin , , , MW Chang , , , K Lee , , , 等 . . BERT: pre-training of deep bidirectional transformers for language understanding . . 2018 . . https://arxiv.org/abs/1810.04805 https://arxiv.org/abs/1810.04805 , , . .

S Han , , , HZ Mao , , , WJ Dally . . Deep compression: compressing deep neural network with pruning, trained quantization and Huffman coding . . Proc $4^{\rm th}$ Int Conf on Learning Representations , , 2016 . . p.1 - - 14 . . . .

KM He , , , XY Zhang , , , SQ Ren , , , 等 . . Deep residual learning for image recognition . . IEEE Conf on Computer Vision and Pattern Recognition , , 2016 . . p.770 - - 778 . . DOI: 10.1109/CVPR.2016.90 http://doi.org/10.1109/CVPR.2016.90 . .

AG Howard , , , ML Zhu , , , B Chen , , , 等 . . MobileNets: efficient convolutional neural networks for mobile vision applications . . 2017 . . https://arxiv.org/abs/1704.04861 https://arxiv.org/abs/1704.04861 , , . .

H Hu , , , JY Gu , , , Z Zhang , , , 等 . . Relation networks for object detection . . 2017 . . http://arxiv.org/abs/1711.11575 http://arxiv.org/abs/1711.11575 , , . .

G Huang , , , SC Liu , , , L van der Maaten , , , 等 . . Condensenet: an efficient densenet using learned group convolutions . . 2017 . . https://arxiv.org/abs/1711.09224 https://arxiv.org/abs/1711.09224 , , . .

I Hubara , , , M Courbariaux , , , D Soudry , , , 等 . . Binarized neural networks . . Proc $30^{\rm th}$ Int Conf on Neural Information Processing Systems , , 2016 . . p.4114 - - 4122 . . . .

I Hubara , , , M Courbariaux , , , D Soudry , , , 等 . . Quantized neural networks: training neural networks with low precision weights and activations . . J Mach Learn Res , , 2018 . . 18 ( ( 187 ): ): 1 - - 30 . . . .

S Ioffe , , , C Szegedy . . Batch normalization: accelerating deep network training by reducing internal covariate shift . . Proc $32^{\rm nd}$ Int Conf on Machine Learning , , 2015 . . p.1448 - - 1456 . . . .

S Jégou , , , M Drozdzal , , , D Vazquez , , , 等 . . The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation . . Proc IEEE Conf on Computer Vision and Pattern Recognition Workshops , , 2017 . . p.1175 - - 1183 . . DOI: 10.1109/CVPRW.2017.156 http://doi.org/10.1109/CVPRW.2017.156 . .

H Lee , , , T Matin , , , F Gleeson , , , 等 . . Efficient 3D fully convolutional networks for pulmonary lobe segmentation in CT images . . 2019 . . https://arxiv.org/abs/1909.07474 https://arxiv.org/abs/1909.07474 , , . .

C Li , , , CJR Shi . . Constrained optimization based low-rank approximation of deep neural networks . . Proc $15^{\rm th}$ European Conf on Computer Vision , , 2018 . . p.746 - - 761 . . DOI: 10.1007/978-3-030-01249-6_45 http://doi.org/10.1007/978-3-030-01249-6_45 . .

H Li , , , A Kadav , , , I Durdanovic , , , 等 . . Pruning filters for efficient ConvNets . . 2016 . . https://arxiv.org/abs/1608.08710 https://arxiv.org/abs/1608.08710 , , . .

HC Li , , , PF Xiong , , , HQ Fan , , , 等 . . DFANet: deep feature aggregation for real-time semantic segmentation . . 2019 . . https://arxiv.org/abs/1904.02216 https://arxiv.org/abs/1904.02216 , , . .

GS Lin , , , CH Shen , , , A van den Hengel , , , 等 . . Efficient piecewise training of deep structured models for semantic segmentation . . IEEE Conf on Computer Vision and Pattern Recognition , , 2016 . . p.3194 - - 3203 . . DOI: 10.1109/CVPR.2016.348 http://doi.org/10.1109/CVPR.2016.348 . .

GS Lin , , , FY Liu , , , A Milan , , , 等 . . RefineNet: multi-path refinement networks for dense prediction . . IEEE Trans Patt Anal Mach Intell , , 2019 . . p.1228 - - 1242 . . DOI: 10.1109/TPAMI.2019.2893630 http://doi.org/10.1109/TPAMI.2019.2893630 . .

ZW Liu , , , XX Li , , , P Luo , , , 等 . . Semantic image segmentation via deep parsing network . . IEEE Int Conf on Computer Vision , , 2015 . . p.1377 - - 1385 . . DOI: 10.1109/ICCV.2015.162 http://doi.org/10.1109/ICCV.2015.162 . .

J Long , , , E Shelhamer , , , T Darrell . . Fully convolutional networks for semantic segmentation . . 2014 . . https://arxiv.org/abs/1411.4038 https://arxiv.org/abs/1411.4038 , , . .

NN Ma , , , XY Zhang , , , HT Zheng , , , 等 . . ShuffleNet V2: practical guidelines for efficient CNN architecture design . . Proc $15^{\rm th}$ European Conf on Computer Vision , , 2018 . . p.122 - - 138 . . DOI: 10.1007/978-3-030-01264-9_8 http://doi.org/10.1007/978-3-030-01264-9_8 . .

D Mazzini . . Guided upsampling network for real-time semantic segmentation . . 2018 . . https://arxiv.org/abs/1807.07466 https://arxiv.org/abs/1807.07466 , , . .

S Mehta , , , M Rastegari , , , A Caspi , , , 等 . . ESPNet: efficient spatial pyramid of dilated convolutions for semantic segmentation . . Proc $15^{\rm th}$ European Conf on Computer Vision , , 2018 . . p.561 - - 580 . . DOI: 10.1007/978-3-030-01249-6_34 http://doi.org/10.1007/978-3-030-01249-6_34 . .

S Mehta , , , M Rastegari , , , L Shapiro , , , 等 . . ESPNetv2: a light-weight, power efficient, and general purpose convolutional neural network . . IEEE Conf on Computer Vision and Pattern Recognition , , 2019 . . p.9190 - - 9200 . . DOI: 10.1109/CVPR.2019.00941 http://doi.org/10.1109/CVPR.2019.00941 . .

V Nekrasov , , , CH Shen , , , I Reid . . Light-weight RefineNet for real-time semantic segmentation . . British Machine Vision Conf , , 2018 . . p.125 . .

H Noh , , , S Hong , , , B Han . . Learning deconvolution network for semantic segmentation . . 2015 . . https://arxiv.org/abs/1505.04366 https://arxiv.org/abs/1505.04366 , , . .

Y Pan . . On visual knowledge . . Front Inform Technol Electon Eng , , 2019 . . 20 ( ( 8 ): ): 1021 - - 1025 . . DOI: 10.1631/FITEE.1910001 http://doi.org/10.1631/FITEE.1910001 . .

A Paszke , , , A Chaurasia , , , S Kim , , , 等 . . ENet: a deep neural network architecture for real-time semantic segmentation . . 2016 . . https://arxiv.org/abs/1606.02147 https://arxiv.org/abs/1606.02147 , , . .

YX Peng , , , XT He , , , JJ Zhao . . Object-part attention model for fine-grained image classification . . IEEE Trans Image Process , , 2018 . . 27 ( ( 3 ): ): 1487 - - 1500 . . DOI: 10.1109/TIP.2017.2774041 http://doi.org/10.1109/TIP.2017.2774041 . .

RPK Poudel , , , U Bonde , , , S Liwicki , , , 等 . . ContextNet: exploring context and detail for semantic segmentation in real-time . . 2018 . . https://arxiv.org/abs/1805.04554 https://arxiv.org/abs/1805.04554 , , . .

RPK Poudel , , , S Liwicki , , , R Cipolla . . Fast-SCNN: fast semantic segmentation network . . 2019 . . https://arxiv.org/abs/1902.04502 https://arxiv.org/abs/1902.04502 , , . .

M Rastegari , , , V Ordonez , , , J Redmon , , , 等 . . XNOR-Net: ImageNet classification using binary convolutional neural networks . . Proc $14^{\rm th}$ European Conf on Computer Vision , , 2016 . . p.525 - - 542 . . DOI: 10.1007/978-3-319-46493-0_32 http://doi.org/10.1007/978-3-319-46493-0_32 . .

SQ Ren , , , KM He , , , R Girshick , , , 等 . . Faster R-CNN: towards real-time object detection with region proposal networks . . IEEE Trans Patt Anal Mach Intell , , 2017 . . 39 ( ( 6 ): ): 1137 - - 1149 . . DOI: 10.1109/TPAMI.2016.2577031 http://doi.org/10.1109/TPAMI.2016.2577031 . .

E Romera , , , JM Álvarez , , , LM Bergasa , , , 等 . . ERFNet: efficient residual factorized ConvNet for real-time semantic segmentation . . IEEE Trans Intell Transp Syst , , 2018 . . 19 ( ( 1 ): ): 263 - - 272 . . DOI: 10.1109/TITS.2017.2750080 http://doi.org/10.1109/TITS.2017.2750080 . .

A Salvador , , , M Bellver , , , V Campos , , , 等 . . Recurrent neural networks for semantic instance segmentation . . 2017 . . https://arxiv.org/abs/1712.00617 https://arxiv.org/abs/1712.00617 , , . .

M Sandler , , , A Howard , , , ML Zhu , , , 等 . . MobileNetV2: inverted residuals and linear bottlenecks . . IEEE Conf on Computer Vision and Pattern Recognition , , 2018 . . p.4510 - - 4520 . . DOI: 10.1109/CVPR.2018.00474 http://doi.org/10.1109/CVPR.2018.00474 . .

J Sherrah . . Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery . . 2016 . . https://arxiv.org/abs/1606.02585 https://arxiv.org/abs/1606.02585 , , . .

M Siam , , , M Gamal , , , M Abdel-Razek , , , 等 . . A comparative study of real-time semantic segmentation for autonomous driving . . IEEE Conf on Computer Vision and Pattern Recognition Workshops , , 2018 . . p.587 - - 597 . . DOI: 10.1109/CVPRW.2018.00101 http://doi.org/10.1109/CVPRW.2018.00101 . .

D Soudry , , , I Hubara , , , R Meir . . Expectation backpropagation: parameter-free training of multilayer neural networks with continuous or discrete weights . . Proc $27^{\rm th}$ Int Conf on Neural Information Processing Systems , , 2014 . . p.963 - - 971 . . . .

P Sturgess , , , K Alahari , , , L Ladicky , , , 等 . . Combining appearance and structure from motion features for road scene understanding . . British Machine Vision Conf , , 2009 . . p.1 - - 11 . . DOI: 10.5244/C.23.62 http://doi.org/10.5244/C.23.62 . .

C Szegedy , , , V Vanhoucke , , , S Ioffe , , , 等 . . Rethinking the inception architecture for computer vision . . 2015 . . https://arxiv.org/abs/1512.00567 https://arxiv.org/abs/1512.00567 , , . .

S Türkmen , , , J Heikkilä . . An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions . . Proc $21^{\rm st}$ Scandinavian Conf on Image Analysis , , 2019 . . p.41 - - 53 . . DOI: 10.1007/978-3-030-20205-7_4 http://doi.org/10.1007/978-3-030-20205-7_4 . .

F Visin , , , K Kastner , , , AC Courville , , , 等 . . ReSeg: a recurrent neural network for object segmentation . . 2015 . . https://arxiv.org/abs/1511.07053 https://arxiv.org/abs/1511.07053 , , . .

W Wen , , , CP Wu , , , YD Wang , , , 等 . . Learning structured sparsity in deep neural networks . . Proc $30^{\rm th}$ Int Conf on Neural Information Processing Systems , , 2016 . . p.1 - - 9 . . . .

AC Wilson , , , R Roelofs , , , M Stern , , , 等 . . The marginal value of adaptive gradient methods in machine learning . . Proc $31^{\rm st}$ Int Conf on Neural Information Processing Systems , , 2017 . . p.1 - - 14 . . . .

S Wu , , , GQ Li , , , F Chen , , , 等 . . Training and inference with integers in deep neural networks . . 2018 . . https://arxiv.org/abs/1802.04680 https://arxiv.org/abs/1802.04680 , , . .

W Xiang , , , HD Mao , , , V Athitsos . . ThunderNet: a turbo unified network for real-time semantic segmentation . . IEEE Winter Conf on Applications of Computer Vision , , 2019 . . p.1789 - - 1796 . . DOI: 10.1109/WACV.2019.00195 http://doi.org/10.1109/WACV.2019.00195 . .

J Yang , , , QS Liu , , , KH Zhang . . Stacked hourglass network for robust facial landmark localisation . . IEEE Conf on Computer Vision and Pattern Recognition Workshops , , 2017 . . p.2025 - - 2033 . . DOI: 10.1109/CVPRW.2017.253 http://doi.org/10.1109/CVPRW.2017.253 . .

CQ Yu , , , JB Wang , , , C Peng , , , 等 . . BiSeNet: bilateral segmentation network for real-time semantic segmentation . . Proc $15^{\rm th}$ European Conf on Computer Vision , , 2018 . . p.334 - - 349 . . DOI: 10.1007/978-3-030-01261-8_20 http://doi.org/10.1007/978-3-030-01261-8_20 . .

F Yu , , , V Koltun . . Multi-scale context aggregation by dilated convolutions . . Proc $4^{\rm th}$ Int Conf on Learning Representations , , 2016 . . p.1 - - 13 . . . .

F Yu , , , V Koltun , , , T Funkhouser . . Dilated residual networks . . IEEE Conf on Computer Vision and Pattern Recognition , , 2017 . . p.636 - - 644 . . DOI: 10.1109/CVPR.2017.75 http://doi.org/10.1109/CVPR.2017.75 . .

JC Zhang , , , YX Peng . . Hierarchical vision-language alignment for video captioning . . Proc $25^{\rm th}$ Int Conf on Multimedia Modeling , , 2019a . . p.42 - - 54 . . DOI: 10.1007/978-3-030-05710-7_4 http://doi.org/10.1007/978-3-030-05710-7_4 . .

JC Zhang , , , YX Peng . . Object-aware aggregation with bidirectional temporal graph for video captioning . . 2019b . . https://arxiv.org/abs/1906.04375 https://arxiv.org/abs/1906.04375 , , . .

QS Zhang , , , SC Zhu . . Visual interpretatbility for deep learning: a survey . . Front Inform Technol Electron Eng , , 2018 . . 19 ( ( 1 ): ): 27 - - 39 . . DOI: 10.1631/FITEE.1700808 http://doi.org/10.1631/FITEE.1700808 . .

HS Zhao , , , JP Shi , , , XJ Qi , , , 等 . . Pyramid scene parsing network . . IEEE Conf on Computer Vision and Pattern Recognition , , 2017 . . p.6230 - - 6239 . . DOI: 10.1109/CVPR.2017.660 http://doi.org/10.1109/CVPR.2017.660 . .

HS Zhao , , , XJ Qi , , , XY Shen , , , 等 . . ICNet for real-time semantic segmentation on high-resolution images . . Proc $15^{\rm th}$ European Conf on Computer Vision , , 2018 . . p.418 - - 434 . . DOI: 10.1007/978-3-030-01219-9_25 http://doi.org/10.1007/978-3-030-01219-9_25 . .

S Zheng , , , S Jayasumana , , , B Romera-Paredes , , , 等 . . Conditional random fields as recurrent neural networks . . IEEE Int Conf on Computer Vision , , 2015 . . p.1529 - - 1537 . . DOI: 10.1109/ICCV.2015.179 http://doi.org/10.1109/ICCV.2015.179 . .

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