Semantic segmentation of urban street scene images based on improved U-Net network

Fuzhen ZHU; Jingyi CUI; Bing ZHU; Huiling LI; Yan and LIU

doi:10.1007/s11801-023-2128-8

[1] ZHOU J M, LI B J, CHEN S Z. A real-time segmentation method of road scene based on multi-layer feature fusion[J]. Surveying and mapping bulletin, 2020, (1): 10-15.

[2] MO Y, WU Y, YANG X, et al. Review the state-of-the-art technologies of semantic segmentation based on deep learning[J]. Neurocomputing, 2022, 493: 626-646.

[3] BAI J, HAO P H, CHEN S H. Traffic scene understanding using lightweight convolutional neural network image semantic segmentation[J]. Journal of automotive safety and energy, 2018, 9(04): 433-440.

[4] SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(4): 640-651.

[5] LIU W M, XIN Y L, JIANG X Y. Semantic segmentation of residual network image combined with jump connection[J]. Information technology, 2020, 44(06): 5-9.

[6] YANG C J. Image semantic segmentation based on convolutional neural network[D]. Lanzhou: Northwest Normal University, 2020: 25.

[7] BADRINARAYANAN V, KENDALL A, CIPOLLA R, et al. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(12): 2481-2495.

[8] YU F, KOLTUN V, FUNKHOUSER T. Dilated residual network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Hawaii, USA. New York: IEEE, 2017: 472-480.

[9] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[EB/OL]. (2014-12-22)[2022-06-20]. https: //arxiv.org/abs/1412.7062.

[10] 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, 2016, 40(4): 834-848.

[11] ZHANG Y H, LIU H, TIAN W, et al. A method of rain cloud cluster segmentation in Tibet based on DeepLabV3[J]. Journal of computer applications, 2020, 40(09): 2781-2788.

[12] KUMAR P, SHANKAR H A. Convolutional neural network with batch normalisation for fault detection in squirrel cage induction motor[J]. IET electric power applications, 2021, 15(1): 39-50.

[13] FU J, LIU J, TIAN H, et al. Dual attention network for scene segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 16-20, 2019, Long Beach, CA, USA. New York: IEEE, 2019: 3146-3154.

[14] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional network for biomedical image segmention[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, October 5-9, 2015, Munich, Germany.: Berlin, Heidelberg: Springer-Verlag, 2015: 234-241.

[15] CHEN Z, LI D, FAN W, et al. Self-attention in reconstruction bias U-Net for semantic segmentation of building rooftops in optical remote sensing images[J]. Remote sensing, 2021, 13(13): 2524.

[16] XIAO J Q. Semantic segmentation of road scene based on deep learning[D]. Changchun: Jilin University, 2019: 23-27.

[17] WU T. Research on road scene semantic segmentation algorithm based on fully convolutional neural network[D]. Chongqing: Southwest University, 2020: 14-16.

[18] YU F. Research and implementation of multi-scene image semantic segmentation based on fully convolutional neural network[C]//3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019), March 29-30, 2019, Dalian, China. Paris: Atlantis Press, 2019: 156-161.

[19] CHEN Z, LI D, FAN W, et al. Self-attention in reconstruction bias U-Net for semantic segmentation of building rooftops in optical remote sensing images[J]. Remote sensing, 2021, 13(13): 2524.

[20] LUO P F. Research on semantic segmentation of autonomous driving city scene[D]. Wuhan: Wuhan University, 2019: 16-22.

[21] YUAN X, SHI J, GU L. A review of deep learning methods for semantic segmentation of remote sensing imagery[J]. Expert systems with applications, 2021, 169: 114417.

[22] ZHANG L, HU X, ZHOU Y, et al. Memristive DeepLab: a hardware friendly deep CNN for semantic segmentation[J]. Neurocomputing, 2021, 451: 181-191.