深度学习实时语义分割研究进展和挑战

王卓; 瞿绍军

doi:10.11834/jig.230605

图像/视频语义分割 | 浏览量 : 0 下载量: 262 CSCD: 0

PDF
导出
分享
收藏
专辑

深度学习实时语义分割研究进展和挑战
Research progress and challenges in real-time semantic segmentation for deep learning
2024年29卷第5期页码：1188-1220
收稿日期：2023-08-29，

修回日期：2023-11-17，

纸质出版日期：2024-05-16
DOI： 10.11834/jig.230605
稿件说明：

移动端阅览

王卓，瞿绍军. 2024. 深度学习实时语义分割研究进展和挑战. 中国图象图形学报， 29(05):1188-1220 DOI： 10.11834/jig.230605.

Wang Zhuo， Qu Shaojun. 2024. Research progress and challenges in real-time semantic segmentation for deep learning. Journal of Image and Graphics， 29(05):1188-1220 DOI： 10.11834/jig.230605.

摘要

语义分割作为计算机视觉领域的重要研究方向之一，应用十分广泛。其目的是根据预先定义好的类别对输入图像进行像素级别的分类。实时语义分割则在一般语义分割的基础上又增加了对速度的要求，广泛应用于如无人驾驶、医学图像分析、视频监控与航拍图像等领域。其要求分割方法不仅要取得较高的分割精度，且分割速度也要快。随着深度学习和神经网络的快速发展，实时语义分割也取得了一定的研究成果。本文在前人已有工作的基础上对基于深度学习的实时语义分割算法进行系统的归纳总结，包括基于Transformer和剪枝的方法等，全面介绍实时语义分割方法在各领域中的应用。首先介绍实时语义分割的概念，再根据标签的数量和质量，将现有的基于深度学习的实时语义分割方法分为强监督学习、弱监督学习和无监督学习3个类别。在分类的基础上，结合各个类别中最具有代表性的方法，对其优缺点展开分析，并从多个角度进行比较。随后介绍目前实时语义分割常用的数据集和评价指标，并对比分析各算法在各数据集上的实验效果，阐述现阶段实时语义分割的应用场景。最后，讨论了基于深度学习的实时语义分割存在的挑战，并对实时语义分割未来值得研究的方向进行展望，为研究者们解决存在的问题提供便利。

Abstract

Semantic segmentation is widely used as an important research direction in the field of computer vision， and its purpose is to classify the input image at the pixel level according to predefined categories. Real-time semantic segmentation， as a subfield of semantic segmentation， adds speed requirements to segmentation methods on the basis of general semantic segmentation and is widely used in fields， such as unmanned driving， medical image analysis， video surveillance， and aerial images. The segmentation method should achieve not only high segmentation accuracy but also fast segmentation speed （specifically， the speed of processing images per unit time reaches 30 frames）. With the rapid development of deep learning technology and neural networks， real-time semantic segmentation has also achieved certain research results. Majority of previous researchers have discussed semantic segmentation， but review papers on real-time semantic segmentation methods are few. In this paper， we systematically summarize the real-time semantic segmentation algorithms based on deep learning on the basis of the existing work of the previous researchers. We first introduce the concept of real-time semantic segmentation， and then， according to the number and quality of the participating training labels， the existing real-time semantic segmentation methods based on deep learning are categorized into three classes： strongly supervised learning， weakly supervised learning， and unsupervised learning. Strongly supervised learning methods are categorized from three perspectives： improving accuracy， improving speed， and other methods. Accuracy improvement methods are further divided into subcategories according to the network structure and feature fusion methods. According to the network structure， the real-time semantic segmentation methods can be categorized into encoder-decoder structure， two-branch structure， and multibranch structure； the representative networks in the encoder-decoder section are fully convolutional network （FCN） and UNet； the networks with two-branch structure are the BiSeNet series； and the multibranch structure has ICNet and DFANet. According to the different ways of feature fusion， real-time semantic segmentation methods can be categorized into multiscale feature fusion and attention mechanism. According to the different ways of feature sampling in the process of multiscale feature fusion， this study divides multiscale feature fusion into atrous spatial pyramid pooling and ordinary pyramid pooling； the attention mechanism can be further divided into self-attention mechanism， channel attention， and spatial attention according to the computation method of the attention vector. The methods to improve the speed are analyzed and discussed from the perspectives of improving convolutional blocks and lightweight networks； the methods to improve convolutional blocks can be divided into separable convolution （separable convolution can be divided into depth separable convolution and spatial separable convolution）， grouped convolution， and atrous convolution. Among other methods of strongly supervised learning， we also specifically add methods of knowledge distillation， Transformer-based methods， and pruning， which are less mentioned in other literatures. Given the numerous methods for real-time semantic segmentation based on strongly supervised learning， we also perform a comparative analysis of the strengths and weaknesses of all the mentioned methods. Real-time semantic segmentation based on weakly supervised learning is classified into methods based on image-level labeling， methods based on point labeling， methods based on object box labeling， and methods based on object underlining labeling. The concept of unsupervised learning is introduced， and the commonly used unsupervised semantic segmentation methods at the present stage are described， including the method with the introduction of the generalized domain adaptation problem and the method with the introduction of unsupervised pre-adaptation task. Subsequently， the datasets and evaluation indexes commonly used in real-time semantic segmentation are introduced. In addition to the street scene dataset commonly used in unmanned counting， this study supplements the medical image dataset. In the evaluation indexes， this study provides a detailed introduction to the accuracy measure and speed measure and then compares the experimental effects of the algorithms on the datasets so far through the table to obtain the latest research progress in the field. The application scenarios of real-time semantic segmentation are further elaborated in detail. Real-time semantic segmentation can be applied to automatic driving， which can segment road scene images in a short time to help identify roads， traffic signs， pedestrians， vehicles， and other objects. By segmenting medical images at the pixel level， real-time semantic segmentation can also help doctors identify and localize lesion areas accurately. In the field of natural disaster monitoring and emergency rescue， real-time semantic segmentation can quickly identify airplanes and aircrafts and can help doctors identify and locate lesion areas accurately. Real-time semantic segmentation can quickly recognize disaster areas in aerial images； real-time segmentation of scenes and objects in surveillance videos can provide accurate and intelligent data for surveillance systems. Then， according to the specific application scenarios of real-time semantic segmentation and the problems encountered at this stage， this study considers that the challenges faced by real-time semantic segmentation include the following： 1） mobile segmentation problem， which hardly develops large-scale computation on low-storage devices； 2） how to get away from the dependence of efficient networks on hardware devices； 3） experimental accuracy of the current real-time semantic segmentation model， which hardly reaches the standard of automatic driving； 4） lack of scene data for medical image and 3D point cloud design. Finally， this study gives an outlook on the future directions of real-time semantic segmentation that are worth researching， e.g.， occlusion segmentation， real-time semantic segmentation of small targets， adaptive learning model， cross-modal joint learning， data-centered real-time semantic segmentation， and small-sample real-time semantic segmentation.

关键词

Keywords

references

Abdullah F and Jalal A . 2023 . Semantic segmentation based crowd tracking and anomaly detection via neuro-fuzzy classifier in smart surveillance system . Arabian Journal for Science and Engineering ， 48 （ 2 ）： 2173 - 2190 ［ DOI： 10.1007/s13369-022-07092-x http://dx.doi.org/10.1007/s13369-022-07092-x ］

Arani E ， Marzban S ， Pata A and Zonooz B . 2021 . RGPNet： a real-time general purpose semantic segmentation // Proceedings of 2021 IEEE Winter Conference on Applications of Computer Vision . Waikoloa， USA ： IEEE： 3008 - 3017 ［ DOI： 10.1109/wacv48630.2021.00305 http://dx.doi.org/10.1109/wacv48630.2021.00305 ］

Badrinarayanan V ， Kendall A and Cipolla R . 2017 . SegNet： a deep convolutional encoder-decoder architecture for image segmentation . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 39 （ 12 ）： 2481 - 2495 ［ DOI： 10.1109/TPAMI.2016.2644615 http://dx.doi.org/10.1109/TPAMI.2016.2644615 ］

Bearman A ， Russakovsky O ， Ferrari V and Li F F . 2016 . What’s the point： semantic segmentation with point supervision // Processdings of the 14th European Conference on Computer Vision . Amsterdam， the Netherlands ： Springer： 549 - 565 ［ DOI： 10.1007/978-3-319-46478-7_34 http://dx.doi.org/10.1007/978-3-319-46478-7_34 ］

Bucher M ， Vu T H ， Cord M and Patrick Pérez . 2020 . BUDA： boundless unsupervised domain adaptation in semantic segmentation ［EB/OL］. ［ 2023-08-14 ］. https://arxiv.org/pdf/2004.01130v1.pdf https://arxiv.org/pdf/2004.01130v1.pdf

Cane T and Ferryman J . 2018 . Evaluating deep semantic segmentation networks for object detection in maritime surveillance // Proceedings of the 15th IEEE International Conference on Advanced Video and Signal Based Surveillance . Auckland， New Zealand ： IEEE： 1 - 6 ［ DOI： 10.1109/AVSS.2018.8639077 http://dx.doi.org/10.1109/AVSS.2018.8639077 ］

Chen L C ， Papandreou G ， Kokkinos I ， Murphy K and Yuille A L . 2018a . DeepLab： semantic image segmentation with deep convolutional nets， atrous convolution， and fully connected CRFs . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 40 （ 4 ）： 834 - 848 ［ DOI： 10.1109/TPAMI.2017.2699184 http://dx.doi.org/10.1109/TPAMI.2017.2699184 ］

Chen L C ， Zhu Y K ， Papandreou G ， Schroff F and Adam H . 2018b . Encoder-decoder with atrous separable convolution for semantic image segmentation // Proceedings of the 15th European Conference on Computer Vision . Munich， Germany ： Springer： 833 - 851 ［ DOI： 10.1007/978-3-030-01234-2_49 http://dx.doi.org/10.1007/978-3-030-01234-2_49 ］

Chen W Y ， Gong X Y ， Liu X M ， Zhang Q ， Li Y and Wang Z Y . 2020 . FasterSeg： searching for faster real-time semantic segmentation // Proceedings of the 8th International Conference on Learning Representations . Addis Ababa， Ethiopia ： ICLR

Chen Z ， Peng C X ， Guo W L ， Xie L ， Wang S S ， Zhuge Q C ， Wen C Y and Feng Y J . 2023 . Uncertainty-guided Transformer for brain tumor segmentation . Medical and Biological Engineering and Computing ， 61 （ 12 ）： 3289 - 3301 ［ DOI： 10.1007/s11517-023-02899-8 http://dx.doi.org/10.1007/s11517-023-02899-8 ］

Chen Z ， Xie L ， Chen Y K ， Zeng Q R ， Zhuge Q C ， Shen J K ， Wen C Y and Feng Y J . 2022 . Generative adversarial network based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image . Neurocomputing ， 488 ： 657 - 668 . ［ DOI： 10.1016/j.neucom.2021.11.07 http://dx.doi.org/10.1016/j.neucom.2021.11.07 ］

Chollet F . 2017 . Xception： deep learning with depthwise separable convolutions // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Hawaii， USA ： IEEE： 1800 - 1807 ［ DOI： 10.1109/cvpr.2017.195 http://dx.doi.org/10.1109/cvpr.2017.195 ］

Dai J F ， He K M and Sun J . 2015 . BoxSup： exploiting bounding boxes to supervise convolutional networks for semantic segmentation // Proceedings of 2015 IEEE International Conference on Computer Vision . Santiago， Chile ： IEEE： 1635 - 1643 ［ DOI： 10.1109/iccv.2015.191 http://dx.doi.org/10.1109/iccv.2015.191 ］

Dice L R . 1945 . Measures of the amount of ecologic association between species . Ecology ， 26 （ 3 ）： 297 - 302 ［ DOI： 10.2307/1932409 http://dx.doi.org/10.2307/1932409 ］

Ding J ， Xue N ， Xia G S ， Schiele B and Dai D X . 2023 . HGFormer： hierarchical grouping Transformer for domain generalized semantic segmentation // Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver， Canada ： IEEE： 15413 - 15423 ［ DOI： 10.1109/CVPR52729.2023.01479 http://dx.doi.org/10.1109/CVPR52729.2023.01479 ］

Dosovitskiy A ， Beyer L ， Kolesnikov A ， Weissenborn D ， Zhai X H ， Unterthiner T ， Dehghani M ， Minderer M ， Heigold G ， Gelly S ， Uszkoreit J and Houlsby N . 2021 . An image is worth 16 × 16 words： Transformers for image recognition at scale //Proceedings of the 9th International Conference on Learning Representations. ［s.l.］， Austria ： ICLR

Dong X ， Chen S and Pan S . 2017 . Learning to prune deep neural networks via layer-wise optimal brain surgeon . Advances in Neural Information Processing Systems ， 4857 - 4867 ［ DOI： 10.48550/arXiv.1705.07565 http://dx.doi.org/10.48550/arXiv.1705.07565 ］

Dumoulin V and Visin F . 2016 . A guide to convolution arithmetic for deep learning ［EB/OL］. ［ 2023-08-14 ］. https://arxiv.org/pdf/1603.07285v1.pdf https://arxiv.org/pdf/1603.07285v1.pdf

Elhassan M A M ， Huang C X ， Yang C H and Munea T L . 2021 . DSANet： dilated spatial attention for real-time semantic segmentation in urban street scenes . Expert Systems with Applications ， 183 ： # 115090 ［ DOI： 10.1016/j.eswa.2021.115090 http://dx.doi.org/10.1016/j.eswa.2021.115090 ］

Emara T ， Abd El Munim H E and Abbas H M . 2019 . LiteSeg： a novel lightweight ConvNet for semantic segmentation // Proceedings of 2019 Digital Image Computing： Techniques and Applications . Perth， Australia ： IEEE： 1 - 7 ［ DOI： 10.1109/DICTA47822.2019.8945975 http://dx.doi.org/10.1109/DICTA47822.2019.8945975 ］

Fan J Q ， Wang F ， Chu H Q ， Hu X ， Cheng Y F and Gao B Z . 2023 . MLFNet： multi-level fusion network for real-time semantic segmentation of autonomous driving . IEEE Transactions on Intelligent Vehicles ， 8 （ 1 ）： 756 - 767 ［ DOI： 10.1109/TIV.2022.3176860 http://dx.doi.org/10.1109/TIV.2022.3176860 ］

Fan J S ， Zhang Z X ， Song C F and Tan T N . 2020a . Learning integral objects with intra-class discriminator for weakly-supervised semantic segmentation // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 4282 - 4291 ［ DOI： 10.1109/CVPR42600.2020.00434 http://dx.doi.org/10.1109/CVPR42600.2020.00434 ］

Fan J S ， Zhang Z X ， Tan T N ， Song C F and Xiao J . 2020b . CIAN： cross-image affinity Net for weakly supervised semantic segmentation // Proceedings of the 34th AAAI Conference on Artificial Intelligence . New York， USA ： AAAI： 10762 - 10769 ［ DOI： 10.1609/aaai.v34i07.6705 http://dx.doi.org/10.1609/aaai.v34i07.6705 ］

Fan M Y ， Lai S Q ， Huang J S ， Wei X M ， Chai Z H ， Luo J F and Wei X L . 2021 . Rethinking BiSeNet for real-time semantic segmentation // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Kuala Lumpur， USA ： IEEE： 9711 - 9720 ［ DOI： 10.1109/cvpr46437.2021.00959 http://dx.doi.org/10.1109/cvpr46437.2021.00959 ］

Feng D ， Haase-Schütz C ， Rosenbaum L ， Hertlein H ， Gläser C ， Timm F ， Wiesbeck W and Dietmayer K . 2021 . Deep multi-modal object detection and semantic segmentation for autonomous driving： datasets， methods， and challenges . IEEE Transactions on Intelligent Transportation Systems ， 22 （ 3 ）： 1341 - 1360 ［ DOI： 10.1109/TITS.2020.2972974 http://dx.doi.org/10.1109/TITS.2020.2972974 ］

Gao S H ， Li Z Y ， Yang M H ， Cheng M M ， Han J W and Torr P . 2023 . Large-scale unsupervised semantic segmentation . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 45 （ 6 ）： 7457 - 7476 ［ DOI： 10.1109/TPAMI.2022.3218275 http://dx.doi.org/10.1109/TPAMI.2022.3218275 ］

Gao X ， Li C G and An J B . 2021 . Real-time image semantic segmentation based on attention mechanism and multi-label classification . Journal of Computer-Aided Design and Computer Graphics ， 33 （ 1 ）： 59 - 67

高翔，李春庚，安居白 . 2021 . 基于注意力和多标签分类的图像实时语义分割 . 计算机辅助设计与图形学学报， 33 （ 1 ）： 59 - 67 ［ DOI： 10.3724/SP.J.1089.2021.18233 http://dx.doi.org/10.3724/SP.J.1089.2021.18233 ］

Garcia-Garcia A ， Orts-Escolano S ， Oprea S ， Villena-Martinez V ， Martinez-Gonzalez P and Garcia-Rodriguez J . 2018 . A survey on deep learning techniques for image and video semantic segmentation . Applied Soft Computing ， 70 ： 41 - 65 ［ DOI： 10.1016/j.asoc.2018.05.018 http://dx.doi.org/10.1016/j.asoc.2018.05.018 ］

Geng Q C ， Zhou Z and Cao X C . 2018 . Survey of recent progress in semantic image segmentation with CNNs . Science China Information Sciences ， 61 （ 5 ）： # 051101 ［ DOI： 10.1007/s11432-017-9189-6 http://dx.doi.org/10.1007/s11432-017-9189-6 ］

Guo M H ， Lu C Z ， Liu Z N ， Cheng M M and Hu S M . 2023 . Visual attention network . Computational Visual Media ， 9 （ 4 ）： 733 - 752 ［ DOI： 10.1007/s41095-023-0364-2 http://dx.doi.org/10.1007/s41095-023-0364-2 ］

Guo M H ， Xu T X ， Liu J J ， Liu Z N ， Jiang P T ， Mu T J ， Zhang S H ， Martin R R ， Cheng M M and Hu S M . 2022 . Attention mechanisms in computer vision： a survey . Computational Visual Media ， 8 （ 3 ）： 331 - 368 ［ DOI： 10.1007/s41095-022-0271-y http://dx.doi.org/10.1007/s41095-022-0271-y ］

Guo Y ， Yao A and Chen Y . 2016 . Dynamic network surgery for efficient dnns // Proceedings of the 30th International Conference on Neural Information Processing Systems . Barcelona， Spain ： Curran Associates Inc： 1387 - 1395

Guo Y M ， Liu Y ， Georgiou T and Lew M S . 2018 . A review of semantic segmentation using deep neural networks . International Journal of Multimedia Information Retrieval ， 7 （ 2 ）： 87 - 93 ［ DOI： 10.1007/s13735-017-0141-z http://dx.doi.org/10.1007/s13735-017-0141-z ］

Han S ， Mao H Z and Dally W J . 2015a . Deep compression： compressing deep neural networks with pruning， trained quantization and Huffman coding ［EB/OL］. ［ 2023-08-14 ］. https://arxiv.org/pdf/1510.00149.pdf https://arxiv.org/pdf/1510.00149.pdf

Han S ， Pool J ， Tran J and Daly W J . 2015b . Learning both weights and connections for efficient neural networks // Proceedings of the 28th International Conference on Neural Information Processing Systems . Montreal， Canada ： MIT Press： 1135 - 1143

Hao S J ， Zhou Y and Guo Y R . 2020 . A brief survey on semantic segmentation with deep learning . Neurocomputing ， 406 ： 302 - 321 ［ DOI： 10.1016/j.neucom.2019.11.118 http://dx.doi.org/10.1016/j.neucom.2019.11.118 ］

He J F ， Chen H W and Luo D H . 2023 . Review of real-time semantic segmentation algorithms for deep learning . Computer Engineering and Applications ， 59 （ 8 ）： 13 - 27

何家峰，陈宏伟，骆德汉 . 2023 . 深度学习实时语义分割算法研究综述 . 计算机工程与应用， 59 （ 8 ）： 13 - 27 ［ DOI： 10.3778/j.issn.1002-8331.2210-0144 http://dx.doi.org/10.3778/j.issn.1002-8331.2210-0144 ］

He K M ， Zhang X Y ， Ren S Q and Sun J . 2016 . Deep residual learning for image recognition // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vega， USA ： IEEE： 770 - 778 ［ DOI： 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ］

Holder C J and Shafique M . 2022 . On efficient real-time semantic segmentation： a survey ［EB/OL］. ［ 2023-08-14 ］. https://arxiv.org/pdf/2206.08605.pdf https://arxiv.org/pdf/2206.08605.pdf

Hu J ， Shen L and Sun G . 2018 . Squeeze-and-excitation network // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 7132 - 7141 ［ DOI： 10.1109/CVPR.2018.00745 http://dx.doi.org/10.1109/CVPR.2018.00745 ］

Hu P ， Perazzi F ， Heilbron F C ， Wang O ， Lin Z ， Saenko K and Sclaroff S . 2021 . Real-time semantic segmentation with fast attention . IEEE Robotics and Automation Letters ， 6 （ 1 ）： 263 - 270 ［ DOI： 10.1109/LRA.2020.3039744 http://dx.doi.org/10.1109/LRA.2020.3039744 ］

Hu X G ， Jing L Y and Sehar U . 2022 . Joint pyramid attention network for real-time semantic segmentation of urban scenes . Applied Intelligence ， 52 （ 1 ）： 580 - 594 ［ DOI： 10.1007/s10489-021-02446-8 http://dx.doi.org/10.1007/s10489-021-02446-8 ］

Hu X X ， Yang K L ， Fei L and Wang K W . 2019 . ACNET： attention based network to exploit complementary features for RGBD semantic segmentation // Proceedings of 2019 IEEE International Conference on Image Processing . Taipei， China ： IEEE： 1440 - 1444 ［ DOI： 10.1109/ICIP.2019.8803025 http://dx.doi.org/10.1109/ICIP.2019.8803025 ］

Huang H M ， Lin L F ， Tong R F ， Hu H J ， Zhang Q W ， Iwamoto Y ， Han X H ， Chen Y W and Wu J . 2020 . UNet 3+： a full-scale connected UNet for medical image segmentation // Proceedings of 2020 IEEE International Conference on Acoustics， Speech and Signal Processing . Barcelona， Spain ： IEEE： 1055 - 1059 ［ DOI： 10.1109/ICASSP40776.2020.9053405 http://dx.doi.org/10.1109/ICASSP40776.2020.9053405 ］

Isensee F ， Kickingereder P ， Wick W ， Bendszus M and Maier-Hein K H . 2019 . No new-Net // 4th International Workshop on Brainlesion： Glioma， Multiple Sclerosis， Stroke and Traumatic Brain Injuries . Granada， Spain ： Springer： 234 - 244 ［ DOI： 10.1007/978-3-030-11726-9_21 http://dx.doi.org/10.1007/978-3-030-11726-9_21 ］

Jiang F ， Grigorev A ， Rho S ， Tian Z H ， Fu Y S ， Jifara W ， Adil K and Liu S H . 2018 . Medical image semantic segmentation based on deep learning . Neural Computing and Applications ， 29 （ 5 ）： 1257 - 1265 ［ DOI： 10.1007/s00521-017-3158-6 http://dx.doi.org/10.1007/s00521-017-3158-6 ］

Jiang Z Y ， Ding C X ， Liu M F and Tao D C . 2020 . Two-stage cascaded U-Net： 1st place solution to brats challenge 2019 segmentation task // 5th International Workshop on Brainlesion： Glioma， Multiple Sclerosis， Stroke and Traumatic Brain Injuries . Shenzhen， China ： Springer： 231 - 241 ［ DOI： 10.1007/978-3-030-46640-4_22 http://dx.doi.org/10.1007/978-3-030-46640-4_22 ］

Kamnitsas K ， Bai W ， Ferrante E ， McDonagh S ， Sinclair M ， Pawlowski N ， Rajchl M ， Lee M ， Kainz B ， Rueckert D and Glocker B . 2018 . Ensembles of multiple models and architectures for robust brain tumour segmentation // 3rd International Workshop on Brainlesion： Glioma， Multiple Sclerosis， Stroke and Traumatic Brain Injuries . Quebec City， Canada ： Springer： 450 - 462 ［ DOI： 10.1007/978-3-319-75238-9_38 http://dx.doi.org/10.1007/978-3-319-75238-9_38 ］

Krizhevsky A ， Sutskever I and Hinton G E . 2017 . ImageNet classification with deep convolutional neural networks . Communication of the ACM ， 60 （ 6 ）： 84 - 90 ［ DOI： 10.1145/3065386 http://dx.doi.org/10.1145/3065386 ］

Lateef F and Ruichek Y . 2019 . Survey on semantic segmentation using deep learning techniques . Neurocomputing ， 338 ： 321 - 348 ［ DOI： 10.1016/j.neucom.2019.02.003 http://dx.doi.org/10.1016/j.neucom.2019.02.003 ］

Li B ， Shi Y ， Qi Z Q and Chen Z S . 2018a . A survey on semantic segmentation // Proceedings of 2018 IEEE International Conference on Data Mining Workshop . Singapore， Singapore ： IEEE： 1233 - 1240 ［ DOI： 10.1109/ICDMW.2018.00176 http://dx.doi.org/10.1109/ICDMW.2018.00176 ］

Li G ， Yun I ， Kim J and Kim J . 2019a . DABNet： depth-wise asymmetric bottleneck for real-time semantic segmentation ［EB/OL］. ［ 2023-08-14 ］. https://arxiv.org/pdf/1907.11357.pdf https://arxiv.org/pdf/1907.11357.pdf

Li H C ， Xiong P F ， An J and Wang L X . 2018b . Pyramid attention network for semantic segmentation // Proceedings of 2018 British Machine Vision Conference . Newcastle， UK ： BMVC： #285

Li H C ， Xiong P F ， Fan H Q and Sun J . 2019b . DFANet： deep feature aggregation for real-time semantic segmentation // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 9514 - 9523 ［ DOI： 10.1109/cvpr.2019.00975 http://dx.doi.org/10.1109/cvpr.2019.00975 ］

Li J Y ， Zha S ， Chen C ， Ding M ， Zhang T X and Yu H . 2022a . Attention guided global enhancement and local refinement network for semantic segmentation . IEEE Transactions on Image Processing ， 31 ： 3211 - 3223 ［ DOI： 10.1109/TIP.2022.3166673 http://dx.doi.org/10.1109/TIP.2022.3166673 ］

Li R ， Zheng S Y ， Zhang C ， Duan C X ， Wang L B and Atkinson P M . 2021 . ABCNet： attentive bilateral contextual network for efficient semantic segmentation of fine-resolution remotely sensed imagery . ISPRS Journal of Photogrammetry and Remote Sensing ， 181 ： 84 - 98 ［ DOI： 10.1016/j.isprsjprs.2021.09.005 http://dx.doi.org/10.1016/j.isprsjprs.2021.09.005 ］

Li T P ， Wei Y J ， Liu M L ， Yang X L ， Zhang Z Y and Du J . 2023a . Refined division features based on Transformer for semantic image segmentation . International Journal of Intelligent Systems ， 2023 ： # 6358162 ［ DOI： 10.1155/2023/6358162 http://dx.doi.org/10.1155/2023/6358162 ］

Li X T ， Ding H H ， Zhang W W ， Yuan H B ， Pang J M ， Cheng G L ， Chen K ， Liu Z W and Loy C C . 2023b . Transformer-based visual segmentation： a survey ［EB/OL］. ［ 2023-04-02 ］. https://arxiv.org/pdf/2304.09854.pdf https://arxiv.org/pdf/2304.09854.pdf

Li F Y ， Ye B and Qin C . 2023c . Mutual attention mechanism-driven lightweight semantic segmentation network . Journal of Image and Graphics ， 28 （ 7 ）： 2068 - 2080

栗风永，叶彬，秦川 . 2023 . 互注意力机制驱动的轻量级图像语义分割网络 . 中国图象图形学报， 28 （ 7 ）： 2068 - 2080 ［ DOI： 10.11834/jig.211127 http://dx.doi.org/10.11834/jig.211127 ］

Lin D ， Dai J F ， Jia J Y ， He K M and Sun J . 2016 . ScribbleSup： scribble-supervised convolutional networks for semantic segmentation // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas， USA ： IEEE： 3159 - 3167 ［ DOI： 10.1109/CVPR.2016.344 http://dx.doi.org/10.1109/CVPR.2016.344 ］

Lin P W ， Sun P ， Cheng G L ， Xie S R ， Li X and Shi J P . 2020 . Graph-guided architecture search for real-time semantic segmentation // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 4202 - 4211 ［ DOI： 10.1109/CVPR42600.2020.00426 http://dx.doi.org/10.1109/CVPR42600.2020.00426 ］

Liu C X ， Chen L C ， Schroff F ， Adam H ， Hua W ， Yuille A L and Li F F . 2019 . Auto-DeepLab： hierarchical neural architecture search for semantic image segmentation // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 82 - 92 ［ DOI： 10.1109/CVPR.2019.00017 http://dx.doi.org/10.1109/CVPR.2019.00017 ］

Liu J ， Xu X Q ， Shi Y Q ， Deng C and Shi M H . 2022 . RELAXNet： residual efficient learning and attention expected fusion network for real-time semantic segmentation . Neurocomputing ， 474 ： 115 - 127 ［ DOI： 10.1016/j.neucom.2021.12.003 http://dx.doi.org/10.1016/j.neucom.2021.12.003 ］

Liu J ， Zhang F Y ， Zhou Z Y and Wang J J . 2023a . BFMNet： bilateral feature fusion network with multi-scale context aggregation for real-time semantic segmentation . Neurocomputing ， 521 ： 27 - 40 ［ DOI： 10.1016/j.neucom.2022.11.084 http://dx.doi.org/10.1016/j.neucom.2022.11.084 ］

Liu J ， Zhou Q ， Qiang Y ， Kang B ， Wu X F and Zheng B Y . 2020 . FDDWNet： a lightweight convolutional neural network for real-time semantic segmentation // Proceedings of 2020 IEEE International Conference on Acoustics， Speech and Signal Processing . Barcelon， Spain ： IEEE： 2373 - 2377 ［ DOI： 10.1109/icassp40776.2020.9053838 http://dx.doi.org/10.1109/icassp40776.2020.9053838 .］

Liu J B ， He J J ， Zheng Y J ， Yi S ， Wang X G and Li H S . 2021 . A holistically-guided decoder for deep representation learning with applications to semantic segmentation and object detection . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 45 （ 10 ）： 11390 - 11406 ［ DOI： 10.1109/TPAMI.2021.3114342 http://dx.doi.org/10.1109/TPAMI.2021.3114342 ］

Liu Y H ， Zhang Y F ， Wang Y and Mei S H . 2023b . Rethinking Transformers for semantic segmentation of remote sensing images . IEEE Transactions on Geoscience and Remote Sensing ， 61 ： # 5617515 ［ DOI： 10.1109/TGRS.2023.3302024 http://dx.doi.org/10.1109/TGRS.2023.3302024 ］

Lo S Y ， Hang H M ， Chan S W and Lin J J . 2020 . Efficient dense modules of asymmetric convolution for real-time semantic segmentation // Proceedings of the 1st ACM International Conference on Multimedia in Asia . Beijing， China ： ACM： #3366558 ［ DOI： 10.1145/3338533.3366558 http://dx.doi.org/10.1145/3338533.3366558 ］

Long J ， Shelhamer E and Darrell T . 2015 . Fully convolutional networks for semantic segmentation // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston， USA ： IEEE： 3431 - 3440 ［ DOI： 10.1109/CVPR.2015.7298965 http://dx.doi.org/10.1109/CVPR.2015.7298965 ］

Lopez-Montiel M ， Lopez D A and Montiel O . 2023 . JetSeg： efficient real-time semantic segmentation model for low-power GPU-embedded systems ［EB/OL］. ［ 2023-08-14 ］. https://arxiv.org/pdf/2305.11419.pdf https://arxiv.org/pdf/2305.11419.pdf

Ma J L ， Deng Y Y and Ma Z P . 2020 . Review of deep learning segmentation methods for CT images of liver tumors . Journal of Image and Graphics ， 25 （ 10 ）： 2024 - 2046

马金林，邓媛媛，马自萍 . 2020 . 肝脏肿瘤CT图像深度学习分割方法综述 . 中国图象图形学报， 25 （ 10 ）： 2024 - 2046 ［ DOI： 10.11834/jig.200234 http://dx.doi.org/10.11834/jig.200234 ］

Ma Y Z ， Yu L ， Lin F J and Tian S W . 2023 . Cross-scale sampling Transformer for semantic image segmentation . Journal of Intelligent and Fuzzy Systems ， 44 （ 2 ）： 2895 - 2907 ［ DOI： 10.3233/JIFS-220976 http://dx.doi.org/10.3233/JIFS-220976 ］

Marmanis D ， Wegner J D ， Galliani S ， Schindler K ， Datcu M and Stilla U . 2016 . Semantic segmentation of aerial images with an ensemble of CNNs . ISPRS Annals of the Photogrammetry， Remote Sensing and Spatial Information Sciences ， 3 ： 473 - 480 ［ DOI： 10.5194/isprs-annals-III-3-473-2016 http://dx.doi.org/10.5194/isprs-annals-III-3-473-2016 ］

Mazzini D . 2018 . Guided upsampling network for real-time semantic segmentation // Proceedings of 2018 British Machine Vision Conference . Newcastle， UK ： BMVC： #117

McKinley R ， Rebsamen M ， Meier R and Wiest R . 2019 . Triplanar ensemble of 3D-to-2D CNNs with label-uncertainty for brain tumor segmentation // 5th International Workshop on Brainlesion： Glioma， Multiple Sclerosis， Stroke and Traumatic Brain Injuries . Shenzhen， China ： Springer： 379 - 387 ［ DOI： 10.1007/978-3-030-46640-4_36 http://dx.doi.org/10.1007/978-3-030-46640-4_36 ］

Mehta S ， Rastegari M ， Caspi A ， Shapiro L and Hajishirzi H . 2018 . ESPNet： efficient spatial pyramid of dilated convolutions for semantic segmentation // Proceedings of the 15th European Conference on Computer Vision . Munich， Germany ： Springer： 561 - 580 ［ DOI： 10.1007/978-3-030-01249-6_34 http://dx.doi.org/10.1007/978-3-030-01249-6_34 ］

Mehta S ， Rastegari M ， Shapiro L and Hajishirzi H . 2019 . ESPNetv2： a light-weight， power efficient， and general purpose convolutional neural network // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 9182 - 9192 ［ DOI： 10.1109/CVPR.2019.00941 http://dx.doi.org/10.1109/CVPR.2019.00941 ］

Minaee S ， Boykov Y ， Porikli F ， Plaza A ， Kehtarnavaz N and Terzopoulos D . 2021 . Image segmentation using deep learning： a survey . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 44 （ 7 ）： 3523 - 3542 ［ DOI： 10.1109/TPAMI.2021.3059968 http://dx.doi.org/10.1109/TPAMI.2021.3059968 ］

Mo Y J ， Wu Y ， Yang X N ， Liu F L and Liao Y J . 2022 . Review the state-of-the-art technologies of semantic segmentation based on deep learning . Neurocomputing ， 493 ： 626 - 646 ［ DOI：10.1016/j.neucom.2022.01.005］

Myronenko A . 2019 . 3D MRI brain tumor segmentation using autoencoder regularization // 4th International Workshop on Brainlesion： Glioma， Multiple Sclerosis， Stroke and Traumatic Brain Injuries . Granada， Spain ： Springer： 311 - 320 ［ DOI： 10.1007/978-3-030-11726-9_28 http://dx.doi.org/10.1007/978-3-030-11726-9_28 ］

Neupane B ， Horanont T and Aryal J . 2021 . Deep learning-based semantic segmentation of urban features in satellite images： a review and meta-analysis . Remote Sensing ， 13 （ 4 ）： # 808 ［ DOI： 10.3390/rs13040808 http://dx.doi.org/10.3390/rs13040808 ］

Niu R G ， Sun X ， Tian Y ， Diao W H ， Chen K Q and Fu K . 2022 . Hybrid multiple attention network for semantic segmentation in aerial images . IEEE Transactions on Geoscience and Remote Sensing ， 60 ： 1 - 18 ［ DOI： 10.1109/TGRS.2021.3065112 http://dx.doi.org/10.1109/TGRS.2021.3065112 ］

Oršic M ， Krešo I ， Bevandic P and Šegvic S . 2019 . In defense of pre-trained ImageNet architectures for real-time semantic segmentation of road-driving images // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 12599 - 12608 ［ DOI： 10.1109/CVPR.2019.01289 http://dx.doi.org/10.1109/CVPR.2019.01289 ］

Ouyang L ， He X and Qu S J . 2022 . Fully convolutional neural network with attention module for semantic segmentation . Journal of Frontiers of Computer Science and Technology ， 16 （ 5 ）： 1136 - 1145

欧阳柳，贺禧，瞿绍军 . 2022 . 全卷积注意力机制神经网络的图像语义分割 . 计算机科学与探索， 16 （ 5 ）： 1136 - 1145 ［ DOI： 10.3778/j.issn.1673-9418.2105095 http://dx.doi.org/10.3778/j.issn.1673-9418.2105095 ］

Pan H H ， Hong Y D ， Sun W C and Jia Y S . 2023 . Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes . IEEE Transactions on Intelligent Transportation Systems ， 24 （ 3 ）： 3448 - 3460 ［ DOI： 10.1109/TITS.2022.3228042 http://dx.doi.org/10.1109/TITS.2022.3228042 ］

Paszke A ， Chaurasia A ， Kim S and Culurciello E . 2016 . ENet： a deep neural network architecture for real-time semantic segmentation ［EB/OL］. ［ 2023-08-14 ］. https://arxiv.org/pdf/1606.02147.pdf https://arxiv.org/pdf/1606.02147.pdf

Peng J C ， Liu Y ， Tang S Y ， Hao Y Y ， Chu L T ， Chen G W ， Wu Z W ， Chen Z Y ， Yu Z L ， Du Y N ， Dang Q Q ， Lai B H ， Liu Q W ， Hu X G ， Yu D H and Ma Y J . 2022 . PP-LiteSeg： a superior real-time semantic segmentation model ［EB/OL］. ［ 2023-08-14 ］. https://arxiv.org/pdf/2204.02681.pdf https://arxiv.org/pdf/2204.02681.pdf

Piérard S ， Cioppa A ， Halin A ， Vandeghen R ， Zanella M ， Macq B ， Mahmoudi S and Van Droogenbroeck M . 2023 . Mixture domain adaptation to improve semantic segmentation in real-world surveillance // Proceedings of 2023 IEEE/CVF Winter Conference on Applications of Computer Vision . Hawaii， USA ： IEEE： 1 - 10 ［ DOI： 10.1109/WACVW58289.2023.00007 http://dx.doi.org/10.1109/WACVW58289.2023.00007 ］

Poudel R P K ， Liwicki S and Cipolla R . 2019 . Fast-SCNN： fast semantic segmentation network // Proceedings of the 30th British Machine Vision Conference . Cardiff， UK ： BMVC： #289

Qiao J J ， Cheng Z Q ， Wu X ， Li W and Zhang J . 2022 . Real-time semantic segmentation with parallel multiple views feature augmentation // Proceedings of the 30th ACM International Conference on Multimedia . Lisboa， Portugal ： ACM： 6300 - 6308 ［ DOI： 10.1145/3503161.3547786 http://dx.doi.org/10.1145/3503161.3547786 ］

Qin F W ， Shen X L ， Peng Y ， Shao Y L ， Yuan W Q ， Ji Z P and Bai J . 2021 . A real-time semantic segmentation approach for autonomous driving scenes . Journal of Computer-Aided Design and Computer Graphics ， 33 （ 7 ）： 1026 - 1037

秦飞巍，沈希乐，彭勇，邵艳利，袁文强，计忠平，白静 . 2021 . 无人驾驶中的场景实时语义分割方法 . 计算机辅助设计与图形学学报， 33 （ 7 ）： 1026 - 1037 ［ DOI： 10.3724/SP.J.1089.2021.18631 http://dx.doi.org/10.3724/SP.J.1089.2021.18631 ］

Romera E ， Alvarez J M ， Bergasa L M and Arroyo R . 2018 . ERFNet： efficient residual factorized ConvNet for real-time semantic segmentation . IEEE Transactions on Intelligent Transportation Systems ， 19 （ 1 ）： 263 - 272 ［ DOI： 10.1109/TITS.2017.2750080 http://dx.doi.org/10.1109/TITS.2017.2750080 ］

Ronneberger O ， Fischer P and Brox T . 2015 . U-Net： convolutional networks for biomedical image segmentation // Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention . Munich， Germany ： Springer： 234 - 241 ［ DOI： 10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ］

Roth H R ， Shen C ， Oda H ， Oda M ， Hayashi Y ， Misawa K and Mori K . 2018 . Deep learning and its application to medical image segmentation . Medical Imaging Technology ， 36 （ 2 ）： 63 - 71 ［ DOI： 10.11409/mit.36.63 http://dx.doi.org/10.11409/mit.36.63 ］

Saito K ， Watanabe K ， Ushiku Y and Harada T . 2018 . Maximum classifier discrepancy for unsupervised domain adaptation // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Las Vegas， USA ： IEEE： 3723 - 3732 ［ DOI： 10.1109/CVPR.2018.00392 http://dx.doi.org/10.1109/CVPR.2018.00392 ］

Sandler M ， Howard A ， Zhu M L ， Zhmoginov A and Chen L C . 2018 . MobileNetV2： inverted residuals and linear bottlenecks // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 4510 - 4520 ［ DOI： 10.1109/CVPR.2018.00474 http://dx.doi.org/10.1109/CVPR.2018.00474 ］

Sheng P P ， Shi Y L ， Liu X and Jin H . 2022 . LSNet： real-time attention semantic segmentation network with linear complexity . Neurocomputing ， 509 ： 94 - 101 ［ DOI： 10.1016/j.neucom.2022.08.049 http://dx.doi.org/10.1016/j.neucom.2022.08.049 ］

Siam M ， Elkerdawy S ， Jagersand M and Yogamani S . 2017 . Deep semantic segmentation for automated driving： Taxonomy， roadmap and challenges // Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems . Yokohama， Japan ： IEEE： #8317714 ［ DOI： 10.1109/ITSC.2017.8317714 http://dx.doi.org/10.1109/ITSC.2017.8317714 ］

Simonyan K and Zisserman A . 2015 . Very deep convolutional networks for large-scale image recognition // Proceedings of the 3rd International Conference on Learning Representations . San Diego， USA ： ICLR： #1556 ［ DOI： 10.48550/arXiv.1409.1556 http://dx.doi.org/10.48550/arXiv.1409.1556 ］

Singha T ， Pham D S and Krishna A . 2023 . A real-time semantic segmentation model using iteratively shared features in multiple sub-encoders . Pattern Recognition ， 140 ： # 109557 ［ DOI： 10.1016/j.patcog.2023.109557 http://dx.doi.org/10.1016/j.patcog.2023.109557 ］

Sistu G ， Leang I and Yogamani S . 2019 . Real-time joint object detection and semantic segmentation network for automated driving ［EB/OL］. ［ 2023-08-14 ］. https://arxiv.org/pdf/1901.03912.pdf https://arxiv.org/pdf/1901.03912.pdf

Su Z B ， Li W ， Ma Z and Gao R . 2022 . An improved U-Net method for the semantic segmentation of remote sensing images . Applied Intelligence ， 52 （ 3 ）： 3276 - 3288 ［ DOI： 10.1007/s10489-021-02542-9 http://dx.doi.org/10.1007/s10489-021-02542-9 ］

Sun P ， Wu J X ， Li S Y ， Lin P W ， Huang J Z and Li X . 2021 . Real-time semantic segmentation via auto depth， downsampling joint decision and feature aggregation . International Journal of Computer Vision ， 129 （ 5 ）： 1506 - 1525 ［ DOI： 10.1007/s11263-021-01433-3 http://dx.doi.org/10.1007/s11263-021-01433-3 ］

Taghanaki S A ， Abhishek K ， Cohen J P ， Cohen-Adad J and Hamarneh G . 2021 . Deep semantic segmentation of natural and medical images： a review . Artificial Intelligence Review ， 54 （ 1 ）： 137 - 178 ［ DOI： 10.1007/s10462-020-09854-1 http://dx.doi.org/10.1007/s10462-020-09854-1 ］

Tian S ， Yao G Y and Chen S L . 2023 . Faster SCDNet： real-time semantic segmentation network with split connection and flexible dilated convolution . Sensors ， 23 （ 6 ）： # 3112 ［ DOI： 10.3390/s23063112 http://dx.doi.org/10.3390/s23063112 ］

Tian X ， Wang L and Ding Q . 2019 . Review of image semantic segmentation based on deep learning . Journal of Software ， 30 （ 2 ）： 440 - 468

田萱，王亮，丁琪 . 2019 . 基于深度学习的图像语义分割方法综述 . 软件学报， 30 （ 2 ）： 440 - 468 ［ DOI： 10.13328/j.cnki.jos.005659 http://dx.doi.org/10.13328/j.cnki.jos.005659 ］

Ulku I and Akagündüz E . 2022 . A survey on deep learning-based architectures for semantic segmentation on 2D images . Applied Artificial Intelligence ， 36 （ 1 ）： # 2032924 ［ DOI： 10.1080/08839514.2022.2032924 http://dx.doi.org/10.1080/08839514.2022.2032924 ］

Wang D L ， Li N J ， Zhou Y and Mu J Z . 2021 . Bilateral attention network for semantic segmentation . IET Image Processing ， 15 （ 8 ）： 1607 - 1616 ［ DOI： 10.1049/ipr2.12129 http://dx.doi.org/10.1049/ipr2.12129 ］

Wang G T ， Li W Q ， Ourselin S and Vercauteren T . 2018 . Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks // The 3rd International Workshop on Brainlesion： Glioma， Multiple Sclerosis， Stroke and Traumatic Brain Injuries . Quebec City， Canada ： Springer： 178 - 190 ［ DOI： 10.1007/978-3-319-75238-9_16 http://dx.doi.org/10.1007/978-3-319-75238-9_16 ］

Wang N ， Hou Z Q ， Pu L ， Ma S G and Cheng H H . 2022 . Real-time semantic segmentation analysis based on cavity separable convolution and attention mechanism . Journal of Image and Graphics ， 27 （ 4 ）： 1216 - 1225

王囡，侯志强，蒲磊，马素刚，程环环 . 2022 . 空洞可分离卷积和注意力机制的实时语义分割 . 中国图象图形学报， 27 （ 4 ）： 1216 - 1225 ［ DOI： 10.11834/jig.200729 http://dx.doi.org/10.11834/jig.200729 ］

Wang Y ， Zhou Q ， Liu J ， Xiong J ， Gao G W ， Wu X F and Latecki L J . 2019 . LEDNet： a lightweight encoder-decoder network for real-time semantic segmentation // Proceedings of 2019 IEEE International Conference on Image Processing . Taipei， China ： IEEE： 1860 - 1864 ［ DOI： 10.1109/ICIP.2019.8803154 http://dx.doi.org/10.1109/ICIP.2019.8803154 ］

Wang Z and Qu S J . 2023 . Real-time semantic segmentation network based on attention mechanism and multi-scale pooling . Computer Engineering ， 49 （ 10 ）： 222 - 229 ， 238

王卓，瞿绍军 . 2023 . 基于注意力机制与多尺度池化的实时语义分割网络 . 计算机工程， 49 （ 10 ）： 222- 229 ， 238 ［ DOI： 10.19678/j.issn.1000-3428.0065885 http://dx.doi.org/10.19678/j.issn.1000-3428.0065885 ］

Woo S Y ， Park J C ， Lee J Y and Kweon S I . 2018 . CBAM： convolutional block attention module // Proceedings of the 15th European Conference on Computer Vision . Munich， Germany ： Springer： 3 - 19 ［ DOI： 10.1007/978-3-030-01234-2_1 http://dx.doi.org/10.1007/978-3-030-01234-2_1 ］

Wu T Y ， Tang S ， Zhang R ， Cao J and Zhang Y D . 2021 . CGNet： a light-weight context guided network for semantic segmentation . IEEE Transactions on Image Processing ， 30 ： 1169 - 1179 ［ DOI： 10.1109/TIP.2020.3042065 http://dx.doi.org/10.1109/TIP.2020.3042065 ］

Wu Y ， Jiang J Y ， Huang Z M and Tian Y L . 2022 . FPANet： feature pyramid aggregation network for real-time semantic segmentation . Applied Intelligence ， 52 （ 3 ）： 3319 - 3336 ［ DOI： 10.1007/s10489-021-02603-z http://dx.doi.org/10.1007/s10489-021-02603-z ］

Xie E Z ， Wang W H ， Yu Z D ， Anandkumar A ， Alvarez J M and Luo P . 2023 . SegFormer： simple and efficient design for semantic segmentation with Transformers //Proceedings of the 34th International Conference on Neural Information Processing Systems. ［s.l.］： NIPS： 12077 - 12090

Xie S N ， Girshick R ， Doll􀅡r P ， Tu Z W and He K M . 2017 . Aggregated residual transformations for deep neural networks // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 5987 - 5995 ［ DOI： 10.1109/CVPR.2017.634 http://dx.doi.org/10.1109/CVPR.2017.634 ］

Xiong J J ， Po L M ， Yu W Y ， Zhou C ， Xia P F and Ou W F . 2023 . CSRNet： cascaded selective resolution network for real-time semantic segmentation . Expert Systems with Applications ， 211 ： # 118537 ［ DOI： 10.1016/j.eswa.2022.118537 http://dx.doi.org/10.1016/j.eswa.2022.118537 ］

Yang K Y ， Wang Z H ， Yang Z ， Zheng P Y ， Yao S L ， Zhu X H ， Yue Y ， Wang W ， Zhang J and Ma J M . 2022 . RecepNet： network with large receptive field for real-time semantic segmentation and application for blue-green algae . Remote Sensing ， 14 （ 21 ）： # 5315 ［ DOI： 10.3390/rs14215315 http://dx.doi.org/10.3390/rs14215315 ］

Yang R X and Yu Y Y . 2021 . Artificial convolutional neural network in object detection and semantic segmentation for medical imaging analysis . Frontiers in Oncology ， 11 ： # 638182 ［ DOI： 10.3389/fonc.2021.638182 http://dx.doi.org/10.3389/fonc.2021.638182 ］

Yin H ， Xie W B ， Zhang J J ， Zhang Y F ， Zhu W X ， Gao J ， Shao Y and Li Y J . 2023 . Dual context network for real-time semantic segmentation . Machine Vision and Applications ， 34 （ 2 ）： # 22 ［ DOI： 10.1007/s00138-023-01373-7 http://dx.doi.org/10.1007/s00138-023-01373-7 ］

Yu C Q ， Gao C X ， Wang J B ， Yu G ， Shen C H and Sang N . 2021a . BiseNet V2： bilateral network with guided aggregation for real-time semantic segmentation . International Journal of Computer Vision ， 129 （ 11 ）： 3051 - 3068 ［ DOI： 10.1007/s11263-021-01515-2 http://dx.doi.org/10.1007/s11263-021-01515-2 ］

Yu C Q ， Wang J B ， Gao C X ， Yu G ， Shen C H and Sang N . 2020 . Context prior for scene segmentation // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 12413 - 12422 ［ DOI： 10.1109/CVPR42600.2020.01243 http://dx.doi.org/10.1109/CVPR42600.2020.01243 ］

Yu C Q ， Wang J B ， Peng C ， Gao C X ， Yu G and Sang N . 2018 . BiSeNet： bilateral segmentation network for real-time semantic segmentation // Proceedings of the 15th European Conference on Computer Vision . Munich， Germany ： Springer： 325 - 349 ［ DOI： 10.1007/978-3-030-01261-8_20 http://dx.doi.org/10.1007/978-3-030-01261-8_20 ］

Yu C Q ， Xiao B ， Gao C X ， Yuan L ， Zhang L ， Sang N and Wang J D . 2021b . Lite-HRNet： a lightweight high-resolution network // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Kuala Lumpu， USA ： IEEE： 10435 - 10445 ［ DOI： 10.1109/cvpr46437.2021.01030 http://dx.doi.org/10.1109/cvpr46437.2021.01030 ］

Yu F ， Koltun V and Funkhouser T . 2017 . Dilated residual networks // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Hawaii， USA ： IEEE： 636 - 644 ［ DOI： 10.1109/CVPR.2017.75 http://dx.doi.org/10.1109/CVPR.2017.75 ］

Yu Z X ， Qu S J ， He X and Wang Z . 2023 . High-low dimensional feature guided real-time semantic segmentation network . Journal of Computer Applications ， 43 （ 10 ）： 3077 - 3085

虞资兴，瞿绍军，何鑫，王卓 . 2023 . 高低维特征引导的实时语义分割网络 . 计算机应用， 43 （ 10 ）： 3077 - 3085 ［ DOI： 10.11772/j.issn.1001-9081.2022091438 http://dx.doi.org/10.11772/j.issn.1001-9081.2022091438 ］

Yuan X H ， Shi J F and Gu L C . 2021 . A review of deep learning methods for semantic segmentation of remote sensing imagery . Expert Systems with Applications ， 169 ： # 114417 ［ DOI： 10.1016/j.eswa.2020.114417 http://dx.doi.org/10.1016/j.eswa.2020.114417 ］

Yuan Y H ， Chen X K ， Chen X L and Wang J D . 2019 . Segmentation Transformer： object-contextual representations for semantic segmentation ［EB/OL］. ［ 2023-02-15 ］. https://arxiv.org/pdf/1909.11065.pdf https://arxiv.org/pdf/1909.11065.pdf

Zhang B X ， Li W H ， Hui Y M ， Liu J Y and Guan Y Y . 2020a . MFENet： multi-level feature enhancement network for real-time semantic segmentation . Neurocomputing ， 393 ： 54 - 65 ［ DOI： 10.1016/j.neucom.2020.02.019 http://dx.doi.org/10.1016/j.neucom.2020.02.019 ］

Zhang D ， Zhang H W ， Tang J H ， Hua X S and Sun Q R . 2020b . Causal intervention for weakly-supervised semantic segmentation // Proceedings of the 34th International Conference on Neural Information Processing Systems Vancouver ， Canada ： Curran Associates Inc： 655 - 666

Zhang X L ， Du B C ， Wu Z Y and Wan T B . 2022 . LAANet： lightweight attention-guided asymmetric network for real-time semantic segmentation . Neural Computing and Applications ， 34 （ 5 ）： 3573 - 3587 ［ DOI： 10.1007/s00521-022-06932-z http://dx.doi.org/10.1007/s00521-022-06932-z ］

Zhang X Y ， Zhou X Y ， Lin M X and Sun J . 2018 . ShuffleNet： an extremely efficient convolutional neural network for mobile devices // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 6848 - 6856 ［ DOI： 10.1109/CVPR.2018.00716 http://dx.doi.org/10.1109/CVPR.2018.00716 ］

Zhang Y ， Li X R ， Lin M Q ， Chiu B and Zhao M B . 2020c . Deep-recursive residual network for image semantic segmentation . Neural Computing and Applications ， 32 （ 16 ）： 12935 - 12947 ［ DOI： 10.1007/s00521-020-04738-5 http://dx.doi.org/10.1007/s00521-020-04738-5 ］

Zhao H S ， Qi X J ， Shen X Y ， Shi J P and Jia J Y . 2018 . ICNet for real-time semantic segmentation on high-resolution images // Proceedings of the 15th European Conference on Computer Vision . Munich， Germany ： Springer： 405 - 420 ［ DOI： 10.1007/978-3-030-01219-9_25 http://dx.doi.org/10.1007/978-3-030-01219-9_25 ］

Zhao H S ， Shi J P ， Qi X J ， Wang X G and Jia J Y . 2017 . Pyramid scene parsing network // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Hawaii， USA ： IEEE： 6230 - 6239 ［ DOI： 10.1109/CVPR.2017.660 http://dx.doi.org/10.1109/CVPR.2017.660 ］

Zhao S ， Wu X and Tian K W . 2022 . Real-time semantic segmentation network based on improved BiSeNet V1//Proceedings of the 11th International Conference on Networks， Communication and Computing . Beijing， China ： ACM ： 38 - 44 ［ DOI： 10.1145/3579895.3579902 http://dx.doi.org/10.1145/3579895.3579902 ］

Zhao Y X ， Zhang Y M and Liu C L . 2019 . Bag of tricks for 3D MRI brain tumor segmentation // 5th International Workshop on Brainlesion： Glioma， Multiple Sclerosis， Stroke and Traumatic Brain Injuries . Shenzhen， China ： Springer： 210 - 220 ［ DOI： 10.1007/978-3-030-46640-4_20 http://dx.doi.org/10.1007/978-3-030-46640-4_20 ］

Zhou Q ， Wang Y ， Fan Y W ， Wu X F ， Zhang S F ， Kang B and Latecki L J . 2020a . AGLNet： towards real-time semantic segmentation of self-driving images via attention-guided lightweight network . Applied Soft Computing ， 96 ： # 106682 ［ DOI： 10.1016/j.asoc.2020.106682 http://dx.doi.org/10.1016/j.asoc.2020.106682 ］

Zhou Z W ， Siddiquee M M R ， Tajbakhsh N and Liang J M . 2020b . UNet++： redesigning skip connections to exploit multiscale features in image segmentation . IEEE Transactions on Medical Imaging ， 39 （ 6 ）： 1856 - 1867 ［ DOI： 10.1109/TMI.2019.2959609 http://dx.doi.org/10.1109/TMI.2019.2959609 ］

文章被引用时，请邮件提醒。

提交

深度学习的跨视角地理定位方法综述

深度学习实时语义分割综述

高光谱图像智能分类研究综述与展望

走向通用行人重识别：预训练大模型技术在行人重识别的应用综述

针对视觉深度学习模型的物理对抗攻击研究综述