• Laser & Optoelectronics Progress
  • Vol. 59, Issue 8, 0810005 (2022)
Zhiwen Tao and Fu Niu*
Author Affiliations
  • Institute of Logistics Science and Technology,Academy of Systems Engineering, Academy of Military Sciences of Chinese PLA, Beijing 100071, China
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    DOI: 10.3788/LOP202259.0810005 Cite this Article Set citation alerts
    Zhiwen Tao, Fu Niu. Image Segmentation Method of Military Personnel in Multiple Complex Environments Based on U-Net[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0810005 Copy Citation Text show less
    References

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

    [2] Davis L S. A survey of edge detection techniques[J]. Computer Graphics and Image Processing, 4, 248-270(1975).

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

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

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

    [6] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

    [7] 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).

    [8] Chen X L, Zhao J, Chen S Y et al. Grouped double attention network for semantic segmentation[J]. Laser & Optoelectronics Progress, 58, 2210007(2021).

    [9] Li N, Wu Y Y, Liu Y et al. Pedestrian attribute recognition algorithm based on multi-scale attention network[J]. Laser & Optoelectronics Progress, 58, 0410025(2021).

    [10] Liang X Y, Lin H K, Yang H et al. Construction of semantic segmentation dataset of camouflage target image[J]. Laser & Optoelectronics Progress, 58, 0410015(2021).

    [11] Li X, Wang S Q, Li A. A military target recognition algorithm based on unsupervised network[J]. Electronics Optics & Control, 28, 36-39(2021).

    [12] Gu Z W, Cheng J, Fu H Z et al. CE-net: context encoder network for 2D medical image segmentation[J]. IEEE Transactions on Medical Imaging, 38, 2281-2292(2019).

    [13] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).

    [14] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

    [15] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision[C], 2818-2826(2016).

    [16] Howard A G, Zhu M L, Chen B et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. https://arxiv.org/abs/1704.04861

    [17] Li C H, Lu Y. Facial expression recognition based on depthwise separable convolution[J]. Computer Engineering and Design, 42, 1448-1454(2021).

    [18] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[EB/OL]. https://arxiv.org/abs/1409.0473

    [19] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

    [20] Zhu W T, Huang Y F, Zeng L et al. AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy[J]. Medical Physics, 46, 576-589(2019).

    [21] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).

    [22] Milletari F, Navab N, Ahmadi S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C], 565-571(2016).

    Zhiwen Tao, Fu Niu. Image Segmentation Method of Military Personnel in Multiple Complex Environments Based on U-Net[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0810005
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