• Laser & Optoelectronics Progress
  • Vol. 58, Issue 4, 0410015 (2021)
Xinyu Liang1, Haokun Lin2, Hui Yang1, Kaihong Xiao3, and Jichuan Quan1、*
Author Affiliations
  • 1Institute of Command and Control Engineering, Army Engineering University, Nanjing, Jiangsu 210007, China
  • 2College of Software Engineering, Huazhong University of Science & Technology, Wuhan, Hubei 430070, China;
  • 3Unit 73676 of The Chinese People's Liberation Army, Wuxi, Jiangsu 214400, China
  • show less
    DOI: 10.3788/LOP202158.0410015 Cite this Article Set citation alerts
    Xinyu Liang, Haokun Lin, Hui Yang, Kaihong Xiao, Jichuan Quan. Construction of Semantic Segmentation Dataset of Camouflage Target Image[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410015 Copy Citation Text show less
    References

    [1] 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).

    [2] 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).

    [3] Schmitt M, Hughes L H, Qiu C et al[2020-06-23]. SEN12MS: a curated dataset of georeferenced multi-spectral SENTINEL-1/2 imagery for deep learning and data fusion [2020-06-23].https://arxiv., org/abs/1906, 07789.

    [4] Zhang L X. Digital camouflage design and camouflage effect evaluation based on natural background[J]. Journal of Xi'an Technological University, 39, 358(2019).

    [5] Lin T Y, Maire M, Belongie S et al. Microsoft COCO: common objects in context[M]. //Fleet D, Pajdla T, Schiele B, et al. Computer Vision -ECCV 2014. Lecture Notes in Computer Science. Cham: Springer, 8693, 740-755(2014).

    [6] Zhang H R, Li Y B, Xing R K et al. Evaluation of air defense missile infrared camouflage capability based on set pair analysis[J]. Laser & Optoelectronics Progress, 55, 070402(2018).

    [7] Guo T, Hua W S, Liu X et al. Comprehensive evaluation of optical camouflage effect based on hyperspectra[J]. Laser & Optoelectronics Progress, 53, 101002(2016).

    [8] Zhuo L, Chen X Q, Xie Z P et al. Simulation learning method for discovery of camouflage targets based on deep neural networks[J]. Laser & Optoelectronics Progress, 56, 071102(2019).

    [9] 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).

    [10] 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).

    [11] Chen L C, Zhu Y K, 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. Cham: Springer, 11211, 833-851(2018).

    [12] Long J, Shelhamer E, Darrell T[2020-06-25]. Fully convolutional networks for semantic segmentation [2020-06-25].https://arxiv., org/abs/1411, 4038.

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

    [14] Garcia-Garcia A, Orts-Escolano S, Oprea S et al. Asurvey on deep learning techniques for image and video semantic segmentation[J]. Applied Soft Computing, 70, 41-65(2018). http://d.wanfangdata.com.cn/periodical/ce3760b520a74b2e564471ced1210398

    Xinyu Liang, Haokun Lin, Hui Yang, Kaihong Xiao, Jichuan Quan. Construction of Semantic Segmentation Dataset of Camouflage Target Image[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410015
    Download Citation