• Acta Photonica Sinica
  • Vol. 51, Issue 2, 0210009 (2022)
Junying ZENG1, Yucong CHEN1, Xihua LIN1, Chuanbo QIN1、*, Yinbo WANG1, Jingming ZHU1, Lianfang TIAN2, Yikui ZHAI1, and Junying GAN1
Author Affiliations
  • 1Faculty of Intelligent Manufacturing,Wuyi University,Jiangmen,Guangdong 529020,China
  • 2School of Automation Science and Engineering,South China University of Technology,Guangzhou 510640,China
  • show less
    DOI: 10.3788/gzxb20225102.0210009 Cite this Article
    Junying ZENG, Yucong CHEN, Xihua LIN, Chuanbo QIN, Yinbo WANG, Jingming ZHU, Lianfang TIAN, Yikui ZHAI, Junying GAN. An Ultra-lightweight Real-time Segmentation Network of Finger Vein Textures[J]. Acta Photonica Sinica, 2022, 51(2): 0210009 Copy Citation Text show less
    References

    [1] J LONG, E SHELHAMER, T DARRELL. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39, 640-651(2014).

    [2] B VIJAY, K ALEX, C ROBERTO. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495.

    [3] G LIN, A MILAN, C SHEN et al. Refinenet: multi-path refinement networks for high-resolution semantic segmentation, 1925-1934(2017).

    [4] F CHOLLET. Xception: deep learning with depth wise separable convolutions, 1251-1258(2017).

    [5] F N IANDOLA, S HAN, M W MOSKEWICZ et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint, arXiv(2016).

    [6] A G HOWARD, M ZHU, B CHEN et al. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint, arXiv(2017).

    [7] M SANDLER, A HOWARD, M ZHU et al. Mobilenetv2: Inverted residuals and linear bottlenecks, 4510-4520(2018).

    [8] X ZHANG, X ZHOU, M LIN et al. Shufflenet: an extremely efficient convolutional neural network for mobile devices, 6848-6856(2018).

    [9] N MA, X ZHANG, H T ZHENG et al. Shufflenet v2: Practical guidelines for efficient cnn architecture design, 116-131(2018).

    [10] K HAN, Y WANG, Q TIAN et al. Ghostnet: more features from Cheap operations, 1580-1589(2020).

    [11] G HINTON, O VINYALS, J DEAN. Distilling the knowledge in a neural network. arXiv preprint, arXiv(2015).

    [12] S HAN, H MAO, W J DALLY. Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint, arXiv(2015).

    [13] M RASTEGARI, V ORDONEZ, J REDMON et al. Xnor-net: Imagenet classification using binary convolutional neural networks, 525-542(2016).

    [14] W WEN, C XU, C WU et al. Coordinating filters for faster deep neural networks, 658-666(2017).

    [15] A HOWARD, M SANLDER, G CHU et al. Searching for mobilenetv3, 1314-1324(2019).

    [16] M TAN, B CHEN, R PANG et al. Mnasnet: Platform-aware neural architecture search for mobile, 2820-2828(2019).

    [17] B ZOPH, V VASUDEVAN, J SHLENS et al. Learning transferable architectures for scalable image recognition, 8697-8710(2018).

    [18] Jie HU, Li SHEN, Gang SUN et al. Squeeze and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 99(2017).

    [19] S WOO, J PARK, J Y LEE et al. Cbam: Convolutional block attention module, 3-19(2018).

    [20] J HU, L SHEN, S ALBANIE et al. Gather-excite: Exploiting feature context in convolutional neural networks. arXiv preprint, arXiv(2018).

    [21] D MISRA, T NALAMADA, A U ARASANIPALAI et al. Rotate to attend: Convolutional triplet attention module, 3139-3148(2021).

    [22] Y CHEN, Y KALANTIDIS, J Li et al. A2-nets: double attention networks. arXiv preprint, arXiv(2018).

    [23] Z HUANG, X WANG, L HUANG et al. Ccnet: criss-cross attention for semantic segmentation, 603-612(2019).

    [24] Q HOU, D ZHOU, J FENG. Coordinate attention for efficient mobile network design. arXiv preprint, arXiv(2021).

    [25] O RONNEBERGER, P FISCHER, T BROX. U-net: convolutional networks for biomedical image segmentation, 234-241(2015).

    [26] Z DAQUAN, Q HOU, Y CHEN et al. Rethinking bottleneck structure for efficient mobile network design. arXiv preprint, arXiv(2020).

    [27] Q WANG, B WU, P ZHU et al. ECA-Net: efficient channel attention for deep convolutional neural networks. arXiv preprint, arXiv(2020).

    [28] J ZENG, B ZHU, Y HUANG et al. Real-time segmentation method of lightweight network for finger vein using embedded terminal technique. IEEE Access, 9, 303-316(2020).

    [29] Haoyang ZHOU, Bao FENG, Feifei QI et al. Combining MRF energy and DCE-MRI time-domain features for breast tumors segmentation algorithm. Acta Photonica Sinica, 50, 0610002(2021).

    [30] Hong HUANG, Rongfei LV, Junli TAO et al. Segmentation of lung nodules in CT images using improved U-Net++. Acta Photonica Sinica, 50, 0210001(2021).

    Junying ZENG, Yucong CHEN, Xihua LIN, Chuanbo QIN, Yinbo WANG, Jingming ZHU, Lianfang TIAN, Yikui ZHAI, Junying GAN. An Ultra-lightweight Real-time Segmentation Network of Finger Vein Textures[J]. Acta Photonica Sinica, 2022, 51(2): 0210009
    Download Citation