RETRACTED: A Review of Real-Time Semantic Segmentation Methods for 2D Data in the Context of Deep Learning

Authors

  • Meng Gao Henan Polytechnic University
  • Haifeng Sima Henan Polytechnic University

DOI:

https://doi.org/10.4108/eetel.8433

Keywords:

Image Semantic Segmentation, Deep Learning, Fully Supervised Learning, 2D Data

Abstract

RETRACTED: The article has been retracted due to misconduct during the peer review process. The retraction notice can be found here: https://doi.org/10.4108/eetel.12185

References

[1] Otsu, N. (1979) A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1): 62–66.

[2] Meyer, F. and Beucher, S. (1990) Morphological segmentation. Journal of Visual Communication and Image Representation 1(1): 21–46.

[3] Adams, R. and Bischof, L. (1994) Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(6): 641–647.

[4] Kass, M., Witkin, A. and Terzopoulos, D. (1988) Snakes: Active contour models. International Journal of Computer Vision 1(4): 321–331.

[5] Boykov, Y. and Jolly, M.P. (2001) Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. Proceedings of IEEE International Conference on Computer Vision (ICCV) : 105–112.

[6] Lafferty, J., McCallum, A. and Pereira, F. (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of the 18th International Conference on Machine Learning (ICML) : 282–289.

[7] Li, S.Z. (2009) Markov random field modeling in image analysis (Springer Science & Business Media).

[8] Long, J., Shelhamer, E. and Darrell, T. (2015) Fully convolutional networks for semantic segmentation. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 3431–3440.

[9] Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25.

[10] Ronneberger, O., Fischer, P. and Brox, T. (2015) U-net: Convolutional networks for biomedical image segmentation. In Navab, N., Hornegger, J., Wells, W.M. and Frangi, A.F. [eds.] Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (Cham: Springer International Publishing): 234–241.

[11] 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.

[12] Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K.P. and Yuille, A.L. (2014) Semantic image segmen-tation with deep convolutional nets and fully con-nected crfs. CoRR abs/1412.7062. URL https://api. semanticscholar.org/CorpusID:1996665.

[13] Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K. and Yuille, A.L. (2018) 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.

[14] Chen, L., Papandreou, G., Schroff, F. and Adam, H. (2017) Rethinking atrous convolution for semantic image segmentation. CoRR abs/1706.05587. URL http: //arxiv.org/abs/1706.05587. 1706.05587.

[15] Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F. and Adam, H. (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In European Conference on Computer Vision. URL https: //api.semanticscholar.org/CorpusID:3638670.

[16] Zhao, H., Shi, J., Qi, X., Wang, X. and Jia, J. (2017) Pyramid scene parsing network. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 6230–6239. doi:10.1109/CVPR.2017.660.

[17] Lin, T., Dollár, P., Girshick, R.B., He, K., Hariharan, B. and Belongie, S.J. (2016) Feature pyramid networks for object detection. CoRR abs/1612.03144. URL http: //arxiv.org/abs/1612.03144. 1612.03144.

[18] Vaswani, A. (2017) Attention is all you need. Advances in Neural Information Processing Systems .

[19] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weis-senborn, D., Zhai, X., Unterthiner, T., Dehghani, M. et al. (2020) An image is worth 16x16 words: Transformers for image recognition at scale. CoRR abs/2010.11929. URL https://arxiv.org/abs/2010. 11929. 2010.11929.

[20] Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y. et al. (2021) Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): 6877–6886. doi:10.1109/CVPR46437.2021.00681.

[21] Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M. and Luo, P. (2021) Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in neural information processing systems 34: 12077–12090.

[22] Xu, L., Bennamoun, M., Boussaid, F., Laga, H., Ouyang, W. and Xu, D. (2024) Mctformer+: Multi-class token transformer for weakly supervised semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 46(12): 8380–8395. doi:10.1109/TPAMI.2024.3404422.

[23] Paszke, A., Chaurasia, A., Kim, S. and Culurciello, E. (2016) Enet: A deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147 .

[24] Romera, E., Álvarez, 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.

[25] Xu, Z., Wu, D., Yu, C., Chu, X., Sang, N. and Gao, C. (2024), Sctnet: Single-branch cnn with transformer semantic information for real-time segmentation. URL https://arxiv.org/abs/2312.17071. 2312.17071.

[26] Yu, C., Wang, J., Peng, C., Gao, C., Yu, G. and Sang, N. (2018) Bisenet: Bilateral segmentation network for real-time semantic segmentation. In Proceedings of the European conference on computer vision (ECCV): 325–341.

[27] Yu, C., Gao, C., Wang, J., Yu, G., Shen, C. and Sang, N. (2020) Bisenet V2: bilateral network with guided aggregation for real-time semantic segmentation. CoRR abs/2004.02147. URL https://arxiv.org/abs/2004. 02147. 2004.02147.

[28] Fan, M., Lai, S., Huang, J., Wei, X., Chai, Z., Luo, J. and Wei, X. (2021) Rethinking bisenet for real-time semantic segmentation. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): 9711–9720. doi:10.1109/CVPR46437.2021.00959.

[29] Poudel, R.P.K., Liwicki, S. and Cipolla, R. (2019) Fast-scnn: Fast semantic segmentation network. CoRR abs/1902.04502. URL http://arxiv.org/abs/1902. 04502. 1902.04502.

[30] Pan, H., Hong, Y., Sun, W. and Jia, Y. (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.

[31] Wang, J., Gou, C., Wu, Q., Feng, H., Han, J., Ding, E. and Wang, J. (2022), Rtformer: Efficient design for real-time semantic segmentation with transformer. URL https://arxiv.org/abs/2210.07124. 2210.07124.

[32] Wan, Q., Huang, Z., Lu, J., Yu, G. and Zhang, L. (2024), Seaformer++: Squeeze-enhanced axial transformer for mobile visual recognition. URL https://arxiv.org/abs/2301.13156. 2301.13156.

[33] Zhao, H., Qi, X., Shen, X., Shi, J. and Jia, J. (2018) Icnet for real-time semantic segmentation on high-resolution images. In Proceedings of the European conference on computer vision (ECCV): 405–420.

[34] Mehta, S., Rastegari, M., Caspi, A., Shapiro, L. and Hajishirzi, H. (2018) Espnet: Efficient spatial pyramid of dilated convolutions for semantic segmentation. In Proceedings of the european conference on computer vision (ECCV): 552–568.

[35] Li, H., Xiong, P., Fan, H. and Sun, J. (2019) Dfanet: Deep feature aggregation for real-time semantic seg-mentation. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): 9514–9523. doi:10.1109/CVPR.2019.00975.

[36] Xu, J., Xiong, Z. and Bhattacharyya, S.P. (2023), Pidnet: A real-time semantic segmentation network inspired by pid controllers. URL https://arxiv.org/abs/2206. 02066. 2206.02066.

[37] Dong, B., Wang, P. and Wang, F. (2023), Head-free lightweight semantic segmentation with lin-ear transformer. URL https://arxiv.org/abs/2301. 04648. 2301.04648.

[38] Zhang, W., Huang, Z., Luo, G., Chen, T., Wang, X., Liu, W., Yu, G. et al. (2022), Topformer: Token pyramid transformer for mobile semantic segmentation. URL https://arxiv.org/abs/2204.05525. 2204.05525.

[39] Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U. et al.(2016) The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition: 3213–3223.

[40] Brostow, G.J., Shotton, J., Fauqueur, J. and Cipolla, R. (2008) Segmentation and recognition using structure from motion point clouds. In ECCV (1): 44–57.

[41] Brostow, G.J., Fauqueur, J. and Cipolla, R. (2009) Semantic object classes in video: A high-definition ground truth database. Pattern recognition letters 30(2): 88–97.

[42] Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A. and Torralba, A. (2017) Scene parsing through ade20k dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 5122–5130. doi:10.1109/CVPR.2017.544.

[43] Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Bar-riuso, A. and Torralba, A. (2018), Semantic understand-ing of scenes through the ade20k dataset. URL https: //arxiv.org/abs/1608.05442. 1608.05442.

[44] Caesar, H., Uijlings, J. and Ferrari, V. (2018) Coco-stuff: Thing and stuff classes in context. In Computer vision and pattern recognition (CVPR), 2018 IEEE conference on (IEEE).

[45] Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J. and Zisserman, A. (2010) The pascal visual object classes (voc) challenge. International Journal of Computer Vision 88(2): 303–338.

Downloads

Published

25-02-2025

How to Cite

1.
Gao M, Sima H. RETRACTED: A Review of Real-Time Semantic Segmentation Methods for 2D Data in the Context of Deep Learning. EAI Endorsed Trans e-Learn [Internet]. 2025 Feb. 25 [cited 2026 Apr. 1];11. Available from: https://publications.eai.eu/index.php/el/article/view/8433

Most read articles by the same author(s)