Acta Photonica Sinica, Volume. 51, Issue 2, 0210009(2022)
An Ultra-lightweight Real-time Segmentation Network of Finger Vein Textures
Figures & Tables(20)
Fig. 3. The basic module structure after joining the Cheap operation
Fig. 8. The actual segmentation effect diagram of SGUnet and each lightweight network on the SDU-FV dataset
Fig. 9. The actual segmentation effect diagram of SGUnet and each lightweight network on the MMCBNU_6000 dataset
Fig. 10. Comparison of important indicators with classic lightweight networks
Table 1. Improved network SGUnet and basic Unet，MobileV2+Unet performance comparison table
View tableView in ArticleTable 1. Improved network SGUnet and basic Unet，MobileV2+Unet performance comparison table
Network Params Mult-Adds Dice AUC Accuracy Specificity Precision Unet 13.39M 1.928G 0.444 6 0.843 4 91.16% 96.47% 53.79% MobileV2+Unet 5.289M 171.226M 0.502 5 0.855 4 91.31% 95.34% 53.68% SGUnet 516.014k 39.494M 0.503 0 0.898 2 91.89% 95.95% 57.27%
Table 2. Performance comparison table of ECA module，classic SE module and CA attention module added on the basis of SGUnet
View tableView in ArticleTable 2. Performance comparison table of ECA module，classic SE module and CA attention module added on the basis of SGUnet
Network Dice AUC Accuracy Specificity Precision SGUnet 0.503 0 0.898 2 91.89% 95.95% 57.27% SGUnet+SE 0.496 1 0.892 6 91.95% 96.32% 58.27% SGUnet+CA-32 0.496 8 0.886 7 91.89% 96.25% 57.79% SGUnet+CA-16 0.500 8 0.891 7 91.93% 96.18% 57.87% SGUnet+CA-8 0.501 5 0.886 9 92.00% 96.30% 58.55% SGUnet+ECA-3 0.497 8 0.891 3 91.80% 96.11% 58.37% SGUnet+ECA-5 0.503 8 0.898 2 92.10% 96.34% 59.04%
Table 3. Comparison of network performance of three different schemes a，b，and c
View tableView in ArticleTable 3. Comparison of network performance of three different schemes a，b，and c
Network Dice AUC Accuracy Specificity Precision a 0.501 6 0.898 2 92.10% 96.34% 59.04% b 0.501 2 0.896 4 92.03% 96.18% 58.54% c 0.500 4 0.894 5 91.97% 96.26% 58.32%
Table 4. Comparison of parameters between SGUnetV2 and other networks
View tableView in ArticleTable 4. Comparison of parameters between SGUnetV2 and other networks
Network Params Mult-Adds Dice AUC Accuracy Specificity Precision Unet 13.39M 1.928G 0.444 6 0.843 4 91.16% 96.47% 53.79% MobileV1+Unet 3.932M 481.35M 0.498 9 0.855 4 91.31% 95.34% 53.68% MobileV2+Unet 5.289M 171.226M 0.502 5 0.884 6 91.54% 95.87% 56.68% SGUnetV1 516.054k 39.504M 0.503 8 0.898 2 92.10% 96.34% 59.04% SGUnetV2 416.752k 26.575M 0.499 2 0.898 9 91.73% 96.12% 56.14%
Table 5. Experimental results of SGUnet series network and large-scale network on SDU-FV data set
View tableView in ArticleTable 5. Experimental results of SGUnet series network and large-scale network on SDU-FV data set
Network Params Mult-Adds Dice AUC Accuracy Specificity Precision R2UNet 48.92M ----- ----- 0.902 9 91.87% 98.21% 62.18% DUNet 26.73M ----- ----- 0.913 3 91.99% 97.26% 64.20% Unet 13.39M 1.928G 0.444 6 0.843 4 91.17% 96.48% 53.79% SGUnetV1 516.054k 39.504M 0.503 8 0.898 2 92.10% 96.34% 59.04% SGUnetV2 416.752k 26.575M 0.499 2 0.898 9 91.73% 96.12% 56.14% SGUnetV3 145.25k 10.453M 0.497 3 0.899 2 91.60% 95.81% 55.34%
Table 6. Experimental results of SGUnet series network and large-scale network on MMCBNU_6000 data set
View tableView in ArticleTable 6. Experimental results of SGUnet series network and large-scale network on MMCBNU_6000 data set
Network Params Mult-Adds Dice AUC Accuracy Specificity Precision R2UNet 48.92M ----- ----- 0.905 8 92.94% 97.22% 54.68% DUNet 26.73M ----- ----- 0.912 5 93.30% 97.89% 58.82% Unet 13.39M 1.928G 0.437 2 0.847 4 91.03% 95.70% 49.49% SGUnetV1 516.054k 39.504M 0.538 4 0.934 4 94.11% 96.79% 60.44% SGUnetV2 416.752k 26.575M 0.527 9 0.935 4 93.75% 96.31% 57.55% SGUnetV3 145.25k 10.453M 0.520 2 0.933 3 93.68% 96.36% 57.23%
Table 7. Experimental data of SGUnet series network and other lightweight networks on SDU-FV dataset
View tableView in ArticleTable 7. Experimental data of SGUnet series network and other lightweight networks on SDU-FV dataset
Network Params FLops Mult-Adds Dice AUC Accuracy Specificity Precision Unet 13.39M 1.95G 1.928G 0.444 6 0.843 4 91.17% 96.48% 53.79% Squeeze_Unet 2.893M 296.05M 287.61M 0.501 7 0.863 0 91.02% 94.72% 51.90% Mobile_Unet 3.932M 498.13M 481.35M 0.502 5 0.855 4 91.31% 95.34% 53.68% Ghost_Unet 6.783M 130.46M 128.97M 0.485 3 0.886 4 91.84% 96.75% 58.47% Shuffle_Unet 516K 68.27M 57.97M 0.511 6 0.885 9 91.48% 95.28% 54.49% SGUnetV1 516.054k 42.97M 39.504M 0.503 8 0.898 2 92.10% 96.34% 59.04% SGUnetV2 416.752k 29.26M 26.575M 0.499 2 0.898 9 91.73% 96.12% 56.14% SGUnetV3 145.25k 13.13M 10.453M 0.497 3 0.899 2 91.60% 95.81% 55.34%
Table 8. Experimental data of SGUnet series network and other lightweight networks on MMCBNU_6000 dataset
View tableView in ArticleTable 8. Experimental data of SGUnet series network and other lightweight networks on MMCBNU_6000 dataset
Network Params FLops Mult-Adds Dice AUC Accuracy Specificity Precision Unet 13.39M 1.95G 1.928G 0.474 1 0.883 4 92.42% 95.80% 49.24% Squeeze_Unet 2.893M 296.05M 287.61M 0.510 5 0.921 6 93.08% 95.34% 52.60% Mobile_Unet 3.932M 498.13M 481.35M 0.500 3 0.905 4 92.06% 94.52% 53.37% Ghost_Unet 6.783M 130.46M 128.97M 0.511 0 0.924 3 93.01% 96.43% 58.38% Shuffle_Unet 516K 68.27M 57.97M 0.479 2 0.906 6 91.80% 94.15% 46.33% SGUnetV1 516.054k 42.97M 39.504M 0.538 4 0.934 4 94.11% 96.79% 60.44% SGUnetV2 416.752k 29.26M 26.575M 0.527 9 0.935 4 93.75% 96.31% 57.55% SGUnetV3 145.25k 13.13M 10.453M 0.520 2 0.933 3 93.68% 96.36% 57.23%
Table 9. The running time of SGUnet series and other lightweight networks to process a single SDU-FV data set image on the NVIDIA embedded platform
View tableView in ArticleTable 9. The running time of SGUnet series and other lightweight networks to process a single SDU-FV data set image on the NVIDIA embedded platform
Network Time/s NANO TX2 NX AGX Squeeze_Unet 5.146 0.686 0.663 0.388 Mobile_Unet 5.699 0.519 0.505 0.288 Ghost_Unet 2.719 0.684 0.722 0.358 Shuffle_Unet 3.208 0.766 0.654 0.426 SGUnetV1 2.735 0.455 0.386 0.283 SGUnetV2 2.789 0.523 0.427 0.302 SGUnetV3 2.827 0.569 0.458 0.306
Table 10. The running time of SGUnet series and other lightweight networks to process a single MMCBNU_6000 data set image on the NVIDIA embedded platform
View tableView in ArticleTable 10. The running time of SGUnet series and other lightweight networks to process a single MMCBNU_6000 data set image on the NVIDIA embedded platform
Network Time/s NANO TX2 NX AGX Squeeze_Unet 5.241 0.679 0.699 0.357 Mobile_Unet 5.435 0.524 0.737 0.284 Ghost_Unet 2.791 0.612 0.734 0.346 Shuffle_Unet 3.221 0.766 0.652 0.418 SGUnetV1 2.779 0.457 0.405 0.270 SGUnetV2 2.873 0.525 0.418 0.290 SGUnetV3 2.928 0.570 0.467 0.291
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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
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Received: Aug. 17, 2021
Accepted: Oct. 13, 2021
Published Online: May. 19, 2022
The Author Email: QIN Chuanbo (tenround@163.com)
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