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An improved YOLO Nano model for dorsal hand vein detection system

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Abstract

To date, venipuncture, the most necessary and fundamental medical means, still remains a challenging task for medical stuff due to significant individual differences in vein condition. Thanks to mature development in near-infrared (NIR) imaging technology, a series of venepuncture auxiliary equipment has been devised and put into use. Yet, previous researches concentrated more on vein pattern segmentation, failing to materialize the identification of veins suitable to puncture in an embedded system. Given the above, we propose an approach to detect and locate the optimal veins fully utilizing the state-of-the-art deep learning and image processing technologies in order to provide a more practical reference. Firstly, a dedicated NIR-based puncturable vein positioning system is designed, realizing collection of dorsal hand vein images as well as the rapid and accurate location of veins suitable to puncture. Secondly, considering the limitations of embedded devices on computation ability and memory, an improved network based on YOLO Nano, named YOLO Nano-Vein, is presented with architecture trimmed, output scales reduced, and an atrous spatial pyramid pooling (ASPP) added. Finally, average precision (AP) is increased from 91.68 to 93.23%, and the detection time and parameters of network are reduced by 22% and 17.5%, respectively, which validates the proposed network achieves higher accuracy with less detection time in comparison with YOLO Nano and YOLOv3, indicating stronger applicability for detection tasks on embedded devices.

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References

  1. Bochkovskiy A, Wang C, Liao HM (2020) YOLOv4: Optimal speed and accuracy of object detection. Available via https://arxiv.org/abs/2004.10934. Accessed July 2020

  2. Brewer RD, Salisbury JK (2010) Visual vein-finding for robotic IV insertion. IEEE Int Conf Rob Autom 4597–4602

  3. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  PubMed  Google Scholar 

  4. Chollet, F (2017) Deep learning with depthwise separable convolutions. IEEE Conf Comput Vis Pattern Recognit 1800–1807

  5. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE Comput Soc Conf Comput Vis Pattern Recognit 1:886–893

    Google Scholar 

  6. Du Y, Pan N, Xu Z, Deng F, Shen Y, Kang H (2020) Pavement distress detection and classification based on YOLO network. Int J Pavement Eng 22(13):1659–1672

    Article  Google Scholar 

  7. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2014) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 47:6–7

    Google Scholar 

  8. Girshick R (2015) Fast R-CNN. IEEE Int Conf Comput Vis 1440–1448

  9. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Conf Comput Vis Pattern Recog 580–587

  10. Hamed G (2020) YOLO based breast masses detection and classification in full-field digital mammograms. Comput Meth Prog Bio 200:1-16

  11. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. IEEE Conf Comput Vis Pattern Recognit 770–778

  12. Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24:881–892

    Article  Google Scholar 

  13. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. Int Conf on Learning Representations Available via http://arxiv.org/abs/1412.6980. Accessed July 2020

  14. LeCun Y, Bengio Y, Hinton G (2015) Deep learning Nat 521:436–444

    CAS  Google Scholar 

  15. Li Y, Qiao Z, Zhang S, Wu Z, Mao X, Kou J, Qi H (2017) A novel method for low-contrast and high-noise vessel segmentation and location in venepuncture. IEEE Trans Med Imag 11:2216-2227

    Google Scholar 

  16. Lowe DG (1999) Object recognition from local scale-invariant features. IEEE Int Conf Comput Vis 2:1150–1157

    Google Scholar 

  17. Mao Q, Sun H, Liu Y, Jia R (2019) Mini-YOLOv3: real-time object detector for embedded applications. IEEE Access 7:133529–133538

    Article  Google Scholar 

  18. Paquit VC, Meriaudeau F, Price JR, Tobin KW (2008) Simulation of skin reflectance images using 3D tissue modeling and multispectral Monte Carlo light propagation. Annu Int Conf IEEE Eng Med Biol Soc 447–450

  19. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: united, real-time object detection. IEEE Conf Comput Vis Pattern Recognit 779–788

  20. Redmon J, Farhadi A (2017) YOLO9000: Better, faster, stronger. IEEE Conf Comput Vis Pattern Recognit 7263–7271

  21. Redmon J,Farhadi A (2018) YOLOv3: an incremental improvement. Available via http://arxiv.org/abs/1804.02767. Accessed July 2020

  22. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137–1149

    Article  PubMed  Google Scholar 

  23. Selvaraju R, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2020) Grad-cam: visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision Int J Comput Vis 128(2):336–359

    Article  Google Scholar 

  24. Shahzad A, Saad NM, Walter N, Malik AS, Meriaudeau F (2014) An efficient method for subcutaneous veins localization using near infrared imaging. Int Conf Intell Adv Syst. https://doi.org/10.1109/ICIAS.2014.6869467

    Article  Google Scholar 

  25. Shahzad A, Walter N, Malik AS, Saad NM, Meriaudeau F (2012) Multispectral venous images analysis for optimum illumination selection. IEEE Int Conf Image Process 2383–2387

  26. Viola P, Jones MJ (2001) Rapid object detection using a boosted cascade of simple features. IEEE Comput Soc Conf Comput Vis Pattern Recognit 1:I511–I518

    Google Scholar 

  27. Wang J, Yang K, Pan Z, Wan G, Li M, Li Y (2018) Minutiae-based weighting aggregation of deep convolutional features for vein recognition. IEEE Access 6:61640–61650

    Article  Google Scholar 

  28. Wang L, Leedham G (2006) Near- and far- infrared imaging for vein pattern biometrics. IEEE Int Conf Video Signal Based Surveill 52–57

  29. Wang X, Wang S, Cao J, Wang Y (2020) Data-driven based tiny-YOLOv3 method for front vehicle detection inducing SPP-net. IEEE Access 8:110227–110236

    Article  Google Scholar 

  30. Wei X, Wei D, Suo D, Jia L, Li Y (2020) Multi-target defect identification for railway track line based on image processing and improved YOLOv3 model. IEEE Access 8:61973–61988

    Article  Google Scholar 

  31. Wong A, Famuori M, Shafiee MJ, Li F, Chwyl B, Chung J (2019) YOLO Nano: a highly compact you only look once convolutional neural network for object detection. Available via https://arxiv.org/abs/1910.01271v1. Accessed July 2020

  32. Wong A, Lin Z, Chwyl B (2019) Attonets: compact and efficient deep neural networks for the edge via human-machine collaborative design. IEEE Comput Soc Conf Comput Vis Pattern Recogn Workshops 684–693

  33. Xu D, Wu Y (2020) Improved YOLO-V3 with DenseNet for multi-scale remote sensing target detection. Sensors 20(15):1–23

  34. Yang L, Liu X, Liu Z (2010) A skeleton extracting algorithm for dorsal hand vein pattern. Int Conf Comput Appl Syst Model 13:V1392–V1395

    Google Scholar 

  35. Zeman HD, Lovhoiden G, Vrancken C, Danish RK (2005) Prototype vein contrast enhancer. Opt Eng 44(8):1–9

  36. Zhou L, Min W, Lin D, Han Q, Liu R (2020) Detecting motion blurred vehicle logo in IoV using filter-DeblurGAN and VL-YOLO. IEEE Trans Veh Technol 69:3604–3614

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. College of Bio-information, Chongqing University of Posts and Telecommunications, Chongqing, 400,065, China

    Yuanyuan Tian, Dechun Zhao & Tian Wang

Authors
  1. Yuanyuan Tian
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  2. Dechun Zhao
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  3. Tian Wang
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Correspondence to Dechun Zhao.

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Tian, Y., Zhao, D. & Wang, T. An improved YOLO Nano model for dorsal hand vein detection system. Med Biol Eng Comput 60, 1225–1237 (2022). https://doi.org/10.1007/s11517-022-02551-x

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  • DOI: https://doi.org/10.1007/s11517-022-02551-x

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