[1] Zhou J M, Li B J, Chen S Z. A real time semantic segmentation method based on multi-level feature fusion[J]. Bulletin of Surveying and Mapping, 10-15(2020).

[2] Deng L Y, Yang M, Liang Z D et al. Fusing geometrical and visual information via superpoints for the semantic segmentation of 3D road scenes[J]. Tsinghua Science and Technology, 25, 498-507(2020). http://www.cnki.com.cn/Article/CJFDTotal-QHDY202004006.htm

[3] Liu S T, Yin F L. The basic principle and its new advances of image segmentation methods based on graph cuts[J]. Acta Automatica Sinica, 38, 911-922(2012).

[4] Tian X, Wang L, Ding Q. Review of image semantic segmentation based on deep learning[J]. Journal of Software, 30, 440-468(2019).

[5] Jing Z W, Guan H Y, Peng D F et al. Survey of research in image semantic segmentation based on deep neural network[J]. Computer Engineering, 46, 1-17(2020).

[6] Wang Y, Zhang H J, Huang H X. A survey of image semantic segmentation algorithms based on deep learning[J]. Application of Electronic Technique, 45, 23-27,36(2019).

[7] Zhang X F, Liu J, Shi Z S et al. Review of deep learning-based semantic segmentation[J]. Laser & Optoelectronics Progress, 56, 150003(2019).

[8] Luo H L, Zhang Y. A survey of image semantic segmentation based on deep network[J]. Acta Electronica Sinica, 47, 2211-2220(2019).

[9] Wang Y R, Chen Q L, Wu J J. Research on image semantic segmentation for complex environments[J]. Computer Science, 46, 36-46(2019).

[10] Kuang H Y, Wu J J. Survey of image semantic segmentation based on deep learning[J]. Computer Engineering and Applications, 55, 12-21,42(2019).

[11] Minaee S, Boykov Y, Porikli F et al. Image segmentation using deep learning: a survey[EB/OL]. (2020-01-15)[ 2020-06-15]. https://export.arxiv.org/pdf/2001.05566

[12] Zhang J Y, Zhao X L, Chen Z. Review of semantic segmentation of point cloud based on deep learning[J]. Laser & Optoelectronics Progress, 57, 040002(2020).

[13] Tian Q C, Meng Y. Image semantic segmentation based on convolutional neural network[J]. Journal of Chinese Computer Systems, 41, 1302-1313(2020).

[14] Khan M W. A survey: image segmentation techniques[J]. International Journal of Future Computer and Communication, 3, 89-93(2014). http://www.ijarse.com/images/fullpdf/1507555300_IETEBanglore213.pdf

[15] Yang Y P, Zhao W D, Wang Z C et al. Research on graph-based Normalized Cut image segmentation method[J]. Computer and Modernization, 113-116(2010).

[16] Zheng Q H, Li W Q, Hu W H et al. An interactive image segmentation algorithm based on graph cut[J]. Procedia Engineering, 29, 1420-1424(2012). http://www.sciencedirect.com/science/article/pii/S1877705812001592

[17] Han X. Research on aotomatic image segmentation algorithm based on Grab Cut[D], 8-9(2018).

[18] Liu L, Shi Z G, Su H R et al. Image segmentation based on higher order Markov random field[J]. Journal of Computer Research and Development, 50, 1933-1942(2013).

[19] Arbeláez P, Maire M, Fowlkes C et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 898-916(2011). http://www.mendeley.com/research/community-detection-hierarchical-image-segmentation-4/

[20] Zhang C J, Xue Z, Zhu X B et al. Boosted random contextual semantic space based representation for visual recognition[J]. Information Sciences, 369, 160-170(2016). http://smartsearch.nstl.gov.cn/paper_detail.html?id=a392e4b5a05471412ac7c155f05b44c8

[21] Pont-Tuset J, Arbeláez P, Barron J T et al. Multiscale combinatorial grouping for image segmentation and object proposal generation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 128-140(2017). http://dx.doi.org/10.1109/tpami.2016.2537320

[22] Elhofi A H, Helaly H A. Comparison between digital and manual marking for toric intraocular lenses: a randomized trial[J]. Medicine, 94, e1618(2015). http://smartsearch.nstl.gov.cn/paper_detail.html?id=f9ce6bbf26cb696d54ca7d8795750c21

[23] Wang C Y, Chen J Z, Li W. Review on superpixel segmentation algorithms[J]. Application Research of Computers, 31, 6-12(2014).

[24] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2017).

[25] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[EB/OL]. (2016-04-30)[2020-06-15]. https://arxiv.org/abs/1511.07122

[26] Chen L C, Papandreou G, Kokkinos I et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[EB/OL]. (2016-06-07)[2020-06-10]. https://arxiv.org/abs/1412.7062v2

[27] Paszke A, Chaurasia A, Kim S et al. ENet: a deep neural network architecture for real-time semantic segmentation[EB/OL]. (2016-06-07)[2020-06-15]. https://arxiv.org/abs/1606.02147

[28] Yu F, Koltun V, Funkhouser T. Dilated residual networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 636-644(2017).

[29] Fang Y C, Li Y F, Tu X K et al. Face completion with hybrid dilated convolution[J]. Signal Processing: Image Communication, 80, 115664(2020). http://www.sciencedirect.com/science/article/pii/S0923596519301304

[30] Dai J F, Qi H Z, Xiong Y W et al. Deformable convolutional networks[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 764-773(2017).

[31] Ghiasi G, Fowlkes C C. Laplacian pyramid reconstruction and refinement for semantic segmentation[M]. // Leibe B, Matas J, Sebe N,et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9907, 519-534(2016).

[32] Arnab A, Jayasumana S, Zheng S et al. Higher order conditional random fields in deep neural networks[M]. // Leibe B, Matas J, Sebe N, et al.Computer vision-ECCV 2016. Lecture notes in computer science, 9906, 524-540(2016).

[33] Vemulapalli R, Tuzel O, Liu M Y et al. Gaussian conditional random field network for semantic segmentation[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 3224-3233(2016).

[34] Shen F L, Gan R, Yan S C et al. Semantic segmentation via structured patch prediction, context CRF and guidance CRF[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 5178-5186(2017).

[35] Jiang J D, Zhang Z J, Huang Y Q et al. Incorporating depth into both CNN and CRF for indoor semantic segmentation[C]. //2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), November 24-26, 2017, Beijing, China., 525-530(2017).

[36] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 936-944(2017).

[37] 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, 40, 834-848(2018). http://europepmc.org/abstract/MED/28463186

[38] Wang P Q, Chen P F, Yuan Y et al. Understanding convolution for semantic segmentation[C]. //2018 IEEE Winter Conference on Applications of Computer Vision (WACV), March 12-15, 2018, Lake Tahoe, NV, USA., 1451-1460(2018).

[39] Chen L C, Zhu Y, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. // Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 833-851(2018).

[40] Yang M K, Yu K, Zhang C et al. DenseASPP for semantic segmentation in street scenes[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 3684-3692(2018).

[41] He J J, Deng Z Y, Qiao Y. Dynamic multi-scale filters for semantic segmentation[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South)., 3561-3571(2019).

[42] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 6230-6239(2017).

[43] Zhao H S, Qi X J, Shen X Y et al. ICNet for real-time semantic segmentation on high-resolution images[M]. //Ferrari V, Hebert M, Sminchisescu C, et al.Computer vision-ECCV 2018. Lecture notes in computer science, 11207, 418-434(2018).

[44] He J J, Deng Z Y, Zhou L et al. Adaptive pyramid context network for semantic segmentation[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 7511-7520(2019).

[45] Wu H K, Zhang J G, Huang K Q et al. FastFCN: rethinking dilated convolution in the backbone for semantic segmentation[EB/OL]. (2019-03-28)[2020-06-15]. https://arxiv.org/abs/1903.11816

[46] Zhao H S, Zhang Y, Liu S et al. PSANet: point-wise spatial attention network for scene parsing[M]. // Ferrari V, Hebert M, Sminchisescu C,et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11213, 270-286(2018).

[47] Yuan J J, Zhang L, Chen Y H. Deep neural network based on attention convolution module for image recognition[J]. Computer Engineering and Applications, 55, 9-16(2019).

[48] Feng S T, Zhuo Z S, Pan D R et al. CcNet: a cross-connected convolutional network for segmenting retinal vessels using multi-scale features[J]. Neurocomputing, 392, 268-276(2020). http://www.sciencedirect.com/science/article/pii/S0925231219304655

[49] Yu C Q, Wang J B, Peng C et al. BiSeNet: bilateral segmentation network for real-time semantic segmentation[M]. // Ferrari V, Hebert M, Sminchisescu C, et al.Computer vision-ECCV 2018. Lecture notes in computer science, 11217, 334-349(2018).

[50] Luo C, Xin W, Li X J et al. ACNET: attention-based convolution network with additional discriminative features for DCM classification[EB/OL]. [2020-06-15]. http://www.researchgate.net/publication/335152368_ACNET_Attention-based_Convolution_Network_with_Additional_Discriminative_Features_for_DCM_Classification_S

[51] Xue H L, Liu C, Wan F et al. DANet: divergent activation for weakly supervised object localization[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea, 6588-6597(2019).

[52] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017). http://users.ics.aalto.fi/perellm1/thesis/summaries_html/node64.html

[53] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. //Navab N, Hornegger J, Wells W, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[54] Wu Z S, Fu W P, Han G N. Road scene understanding based on deep convolutional neural network[J]. Computer Engineering and Applications, 53, 8-15(2017).

[55] Yan Y Y, Qu X X, Zhu Q Y et al. Confidence measure method of classification results based on outlier detection[J]. Journal of Nanjing University (Natural Science), 55, 102-109(2019).

[56] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation[C]. //2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile., 1520-1528(2015).

[57] Li Q B, Su D. Multi-organ abdominal image segmentation based on V-Net[J]. Digital Technology & Application, 89,91(2019).

[58] Lin G S, Milan A, Shen C H et al. RefineNet: multi-path refinement networks for high-resolution semantic segmentation[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 5168-5177(2017).

[59] Tian Z, He T, Shen C H et al. Decoders matter for semantic segmentation: data-dependent decoding enables flexible feature aggregation[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA, 3121-3130(2019).

[60] Yang L, Wu Y X, Wang J L et al. Research on recurrent neural network[J]. Journal of Computer Applications, 38, 1-6,26(2018).

[61] Visin F, Romero A, Cho K et al. ReSeg: a recurrent neural network-based model for semantic segmentation[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 26-July 1, 2016, Las Vegas, NV, USA, 426-433(2016).

[62] Li Z, Gan Y K, Liang X D et al. LSTM-CF: unifying context modeling and fusion with LSTMs for RGB-D scene labeling[M]. // Leibe B, Matas J, Sebe N, et al.Computer vision-ECCV 2016. Lecture notes in computer science, 9906, 541-547(2016).

[63] Liang X D, Shen X H, Feng J S et al. Semantic object parsing with graph LSTM[M]. //Leibe B, Matas J, Sebe N, et al.Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 125-143(2016).

[64] Zheng S, Jayasumana S, Romera-Paredes B et al. Conditional random fields as recurrent neural networks[C]. //2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile., 1529-1537(2015).

[65] Wang K F, Gou C, Duan Y J et al. Generative adversarial networks: the state of the art and beyond[J]. Acta Automatica Sinica, 43, 321-332(2017).

[66] Luc P, Couprie C, Chintala S et al. Semantic segmentation using adversarial networks[EB/OL]. (2016-11-25)[2020-06-15]. https://arxiv.org/abs/1611.08408v1

[67] Xue Y, Xu T, Zhang H et al. SegAN: adversarial network with multi-scale L1 loss for medical image segmentation[J]. Neuroinformatics, 16, 383-392(2018). http://europepmc.org/abstract/MED/29725916

[68] Dai J F, He K M, Sun J. BoxSup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation[C]. //2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile, 1635-1643(2015).

[69] Rajchl M, Lee M C H, Oktay O et al. DeepCut: object segmentation from bounding box annotations using convolutional neural networks[J]. IEEE Transactions on Medical Imaging, 36, 674-683(2017).

[70] Song C F, Huang Y, Ouyang W L et al. Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 3131-3140(2019).

[71] Bearman A, Russakovsky O, Ferrari V et al. What’s the point: semantic segmentation with point supervision[M]. // Leibe B, Matas J, Sebe N, et al.Computer vision-ECCV 2016. Lecture notes in computer science, 9911, 549-565(2016).

[72] Lin D, Dai J F, Jia J Y et al. ScribbleSup: scribble-supervised convolutional networks for semantic segmentation[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA, 3159-3167(2016).

[73] Maninis K K, Caelles S, Pont-Tuset J et al. Deep extreme cut: from extreme points to object segmentation[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, 616-625(2018).

[74] Kolesnikov A, Lampert C H. Seed, expand and constrain: three principles for weakly-supervised image segmentation[M]. //Leibe B, Matas J, Sebe N, et al.Computer vision-ECCV 2016. Lecture notes in computer science, 9908, 695-711(2016).

[75] Huang Z L, Wang X G, Wang J S et al. Weakly-supervised semantic segmentation network with deep seeded region growing[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7014-7023(2018).

[76] Wang Y J, Wang G D, Chen C et al. Multi-scale dilated convolution of convolutional neural network for image denoising[J]. Multimedia Tools and Applications, 78, 19945-19960(2019). http://link.springer.com/article/10.1007/s11042-019-7377-y

[77] Ahn J, Kwak S. Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 4981-4990(2018).

[78] Zhou Y Z, Zhu Y, Ye Q X et al. Weakly supervised instance segmentation using class peak response[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 3791-3800(2018).

[79] Wei Y C, Liang X D, Chen Y P et al. STC: a simple to complex framework for weakly-supervised semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2314-2320(2017). http://doi.ieeecomputersociety.org/10.1109/TPAMI.2016.2636150

[80] Mukhopadhyay S. Stochastic gradient descent for linear systems with sequential matrix entry accumulation[J]. Signal Processing, 171, 107494(2020). http://www.sciencedirect.com/science/article/pii/S0165168420300372

[81] Hong S, Noh H, Han B. Decoupled deep neural network for semi-supervised semantic segmentation[EB/OL]. (2015-06-17)[2020-06-15]. https://arxiv.org/abs/1506.04924

[82] Donahue J, Jia Y Q, Vinyals O et al. DeCAF: a deep convolutional activation feature for generic visual recognition[EB/OL]. (2013-10-06)[2020-06-15]. https://arxiv.org/abs/1310.1531

[83] Tzeng E, Hoffman J, Saenko K et al. Adversarial discriminative domain adaptation[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2962-2971(2017).

[84] Hoffman J, Wang D Q, Yu F et al. FCNs in the wild: pixel-level adversarial and constraint-based adaptation[EB/OL]. (2016-12-08)[2020-06-15]. https://arxiv.org/abs/1612.02649v1

[85] Zhang Y H, Qiu Z F, Yao T et al. Fully convolutional adaptation networks for semantic segmentation[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 6810-6818(2018).

[86] Brostow G J, Fauqueur J, Cipolla R. Semantic object classes in video: a high-definition ground truth database[J]. Pattern Recognition Letters, 30, 88-97(2009). http://dl.acm.org/citation.cfm?id=1465403&CFID=418833479&CFTOKEN=54592737

[87] Geiger A, Lenz P, Stiller C et al. Vision meets robotics: the KITTI dataset[J]. The International Journal of Robotics Research, 32, 1231-1237(2013).

[88] Maddern W, Pascoe G, Linegar C et al. 1 year, 1000 km: the Oxford RobotCar dataset[J]. The International Journal of Robotics Research, 36, 3-15(2017).

[89] 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 (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 3213-3223(2016).

[90] Ros G, Sellart L, Materzynska J et al. The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA, 3234-3243(2016).

[91] Neuhold G, Ollmann T, Bulò S R et al. The mapillary vistas dataset for semantic understanding of street scenes[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy., 5000-5009(2017).

[92] Huang X Y, Wang P, Cheng X J et al. The ApolloScape open dataset for autonomous driving and its application[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2702-2719(2020).

[93] Yu F, Chen H F, Wang X et al. BDD100K: a diverse driving dataset for heterogeneous multitask learning[C]. //2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA, 2633-2642(2020).

[94] Buyval A, Gabdullin A, Mustafin R et al. Realtime vehicle and pedestrian tracking for Didi udacity self-driving car challenge[C]. //2018 IEEE International Conference on Robotics and Automation (ICRA), May 21-25, 2018, Brisbane, QLD, Australia., 2064-2069(2018).

[95] Gu Z C, Li Z H, Di X et al. An LSTM-based autonomous driving model using a Waymo open dataset[J]. Applied Sciences, 10, 2046(2020).

[96] Gudigar A, Chokkadi S, Raghavendra U et al. An efficient traffic sign recognition based on graph embedding features[J]. Neural Computing and Applications, 31, 395-407(2019). http://link.springer.com/article/10.1007/s00521-017-3063-z

[97] Houben S, Stallkamp J, Salmen J et al. Detection of traffic signs in real-world images: the German traffic sign detection benchmark[C]. //The 2013 International Joint Conference on Neural Networks (IJCNN), August 4-9, 2013, Dallas, TX, USA, 1-8(2013).

[98] Zhu Y Y, Zhang C Q, Zhou D Y et al. Traffic sign detection and recognition using fully convolutional network guided proposals[J]. Neurocomputing, 214, 758-766(2016).

[99] Lee E, Kim D. Accurate traffic light detection using deep neural network with focal regression loss[J]. Image and Vision Computing, 87, 24-36(2019). http://www.sciencedirect.com/science/article/pii/S0262885619300538

[100] Song S J, Que Z Q, Hou J J et al. An efficient convolutional neural network for small traffic sign detection[J]. Journal of Systems Architecture, 97, 269-277(2019). http://www.sciencedirect.com/science/article/pii/S1383762118305149

[101] Lu W C, Pang Y W, He Y Q et al. Real-time and accurate semantic segmentation based on separable residual modules[J]. Laser & Optoelectronics Progress, 56, 051005(2019).

[102] Cai Y, Huang X G, Zhang Z A et al. Real-time semantic segmentation algorithm based on feature fusion technology[J]. Laser & Optoelectronics Progress, 57, 021011(2020).

[103] Yang J, Dang J S. Recognition and segmentation of three-dimensional point cloud based on deep cascade convolutional neural network[J]. Optics and Precision Engineering, 28, 1187-1199(2020).

[104] Zhang A W, Liu L L, Zhang X Z. Multi-feature 3D road point cloud semantic segmentation method based on convolutional neural network[J]. Chinese Journal of Lasers, 47, 0410001(2020).