• Optical Instruments
  • Vol. 45, Issue 2, 46 (2023)
Bing WANG, Qi HU*, and Yalin BIAN
Author Affiliations
  • School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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    DOI: 10.3969/j.issn.1005-5630.2023.002.006 Cite this Article
    Bing WANG, Qi HU, Yalin BIAN. An image semantic segmentation algorithm with a two-branch structure[J]. Optical Instruments, 2023, 45(2): 46 Copy Citation Text show less
    References

    [2] LONG J, SHELHAMER E, DARRELL T. Fully convolutional wks f semantic segmentation[C]Proceedings of 2015 IEEE Conference on Computer Vision Pattern Recognition (CVPR). Boston: IEEE, 2015: 3431 3440.

    [3] SIMONYAN K, ZISSERMAN A. Very deep convolutional wks f largescale image recognition[C]Proceedings of 3rd International Conference on Learning Representations. San Diego, 2015: 1 − 14.

    [4] NOH H, HONG S, HAN B. Learning deconvolution wk f semantic segmentation[C]Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Santiago: IEEE, 2015: 1520 1528.

    [5] BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

    [6] YU F, KOLTUN V. Multiscale context aggregation by dilated convolutions[C]Proceedings of the 4th International Conference on Learning Representations. San Juan: ICLR, 2016: 1 13.

    [7] CHEN L C, PAPREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional s fully connected CRFs[C]Proceedings of the 3rd International Conference on Learning Representations. San Diego: ICLR, 2015.

    [8] 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).

    [9] LIN G S, MILAN A, SHEN C H, et al. Refine: multipath refinement wks f highresolution semantic segmentation[C]Proceedings of 2017 IEEE Conference on Computer Vision Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 5168 5177.

    [10] NEKRASOV V, SHEN C H, REID I. Lightweight Refine f realtime semantic segmentation[C]British Machine Vision Conference 2018. Newcastle: BMVC, 2018.

    [11] PASZKE A, CHAURASIA A, KIM S, et al. ENet: a deep neural network architecture for real-time semantic segmentation[J]. arXiv preprint arXiv:, 02147, 2016(1606).

    [12] TREML M, ARJONAMEDINA J, UNTERTHINER T, et al. Speeding up semantic segmentation f autonomous driving[C]Proceedings of the 29th Conference on Neural Infmation Processing Systems Wkshop. Barcelona: MIT Press, 2016: 1 7.

    [13] POHLEN T, HERMANS A, MATHIAS M, et al. Fullresolution residual wks f semantic segmentation in street scenes[C]Proceedings of 2017 IEEE Conference on Computer Vision Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 3309 3318.

    [14] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional wks[C]Proceedings of 2017 IEEE Conference on Computer Vision Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2261 2269.

    [15] HU J, SHEN L, SUN G. Squeezeexcitation wks[C]Proceedings of 2018 IEEECVF Conference on Computer Vision Pattern Recognition. Salt Lake City: IEEE, 2018: 7132 7141.

    [16] LI H F, QIU K J, CHEN L, et al. SCAttNet: semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 18, 905-909(2021).

    [17] IOFFE S, SZEGEDY C. Batch nmalization: accelerating deep wk training by reducing internal covariate shift[C]Proceedings of the 32nd International Conference on Machine Learning. Lille: JMLR. g, 2015: 448 456.

    [18] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning f image recognition[C]Proceedings of 2016 IEEE Conference on Computer Vision Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770 778.

    [19] CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking atrous convolution for semantic image segmentation[J]. arXiv preprint arXiv:, 05587, 2017(1706).

    [21] CDTS M, OMRAN M, RAMOS S, et al. The cityscapes dataset f semantic urban scene understing[C]Proceedings of 2016 IEEE Conference on Computer Vision Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 3213 3223.

    [22] 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).

    [24] IANDOLA F N, HAN S, MOSKEWICZ M W, et al. Squeezenet: alexnet-level accuracy with 50x fewer parameters and <0.5MB model size[J]. arXiv preprint arXiv:, 07360, 2016(1602).

    [25] YU C Q, WANG J B, PENG C, et al. BiSe: bilateral segmentation wk f realtime semantic segmentation[C]Proceedings of the 15th European Conference on Computer Vision. Munich: Springer, 2018: 334 349.