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Volume 44 Issue 7
Jul.  2022
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LI Yueyi, DING Hongchang, ZHANG Lei, ZHAO Changfu, ZHANG Shibo, WANG Aijia. Pupil Diopter Detection Approach Based on Improved YOLOv3[J]. Infrared Technology , 2022, 44(7): 702-708.
Citation: LI Yueyi, DING Hongchang, ZHANG Lei, ZHAO Changfu, ZHANG Shibo, WANG Aijia. Pupil Diopter Detection Approach Based on Improved YOLOv3[J]. Infrared Technology , 2022, 44(7): 702-708.
shu

Pupil Diopter Detection Approach Based on Improved YOLOv3

  • 1.

    College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China

  • 2.

    Chongqing Research Institute, Changchun University of Science and Technology, Chongqing 401135, China

  • 3.

    School of Electrical Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China

  • 4.

    Zhengzhou Tobacco Research Institute China National Tobacco Corporation, Zhengzhou 450001, China

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  • Received Date: August 26, 2021
  • Revised Date: November 28, 2021
    Graphical Abstract
    • Abstract
  • To address the problems of low diopter recognition accuracy and low detection efficiency in the pupil area, this paper proposes a pupil image detection algorithm based on an improved YOLOv3 deep neural network. First, a two-class detection network YOLOv3 base for extracting the main features of the pupil is constructed to strengthen the learning ability of the pupil characteristics. Subsequently, through migration learning, the training model parameters are migrated to YOLOv3-DPDC to reduce the difficulty of model training and poor detection performance caused by the uneven distribution of sample data. Finally, fine-tuning is used to quickly train the YOLOv3 multi-classification network to achieve accurate pupil diopter detection. An experimental test was performed using the 1200 collected infrared pupil images. The results show that the average accuracy of diopter detection using this algorithm is as high as 91.6%, and the detection speed can reach 45 fps; these values are significantly better than those obtained using Faster R-CNN for diopter detection.
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    • References (14)
  • [1]
    薛烽, 李湘宁. 一种基于图像处理的屈光度测量方法[J]. 光电工程, 2009, 36(8): 62-66, 74. https://www.cnki.com.cn/Article/CJFDTOTAL-GDGC200908017.htm

    XUE Feng, LI Xiangning. A method of diopter measurement based on image processing[J]. Photoelectric Engineering, 2009, 36(8): 62-66, 74. https://www.cnki.com.cn/Article/CJFDTOTAL-GDGC200908017.htm
    [2]
    胡志轩. 基于红外图像的眼视力屈光度检测系统[D]. 武汉: 华中师范大学, 2018.

    HU Zhixuan. An Eye Vision Diopter Detection System Based On Infrared Image[D]. Wuhan: Center China Normal University, 2018.
    [3]
    谢娟英, 侯琦, 史颖欢, 等. 蝴蝶种类自动识别研究[J]. 计算机研究与发展, 2018, 55(8): 1609-1618. https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201808003.htm

    XIE Juanying, HOU Qi, SHI Yinghuan, et al. Research on automatic recognition of butterfly species[J]. Computer Research and Development, 2018, 55(8): 1609-1618. https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201808003.htm
    [4]
    Redmon J, Divvala S, Girshick R, et al. You only look once: unified real-time object detection[C]//Proceedings of the Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
    [5]
    Kaur P, Khehra B S, Pharwaha A. Deep transfer learning based multi way feature pyramid network for object detection in images[J]. Mathematical Problems in Engineering, 2021, 2021: 1-13.
    [6]
    Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation[C]//IEEE International Conference on Computer Vision, 2015: 5119-5127.
    [7]
    刘智, 黄江涛, 冯欣. 构建多尺度深度卷积神经网络行为识别模型[J]. 光学精密工程, 2017, 25(3): 799-805. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201703035.htm

    LIU Zhi, HUANG Jiangtao, FENG Xin. Building a multi-scale deep convolutional neural network behavior recognition model[J]. Optics and Precision Engineering, 2017, 25(3): 799-805. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201703035.htm
    [8]
    王娟, 刘嘉润, 李瑞瑞. 基于深度学习的红外相机视力检测算法[J]. 电子测量与仪器学报, 2019, 33(11): 36-43. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201911005.htm

    WANG Juan, LIU Jiarun, LI Ruirui. Infrared camera vision detection algorithm based on deep learning[J]. Journal of Electronic Measurement and Instrument, 2019, 33(11): 36-43. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201911005.htm
    [9]
    LIU Y P, JI X X, PEI S T, et al. Research on automatic location and recognition of insulators in substation based on YOLOv3[J]. High Voltage, 2020, 5(1): 62-68. DOI: 10.1049/hve.2019.0091
    [10]
    Murugan A, Nair S, Kumar K. Detection of skin cancer using SVM, random forest and KNN classifiers[J]. Journal of Medical Systems, 2019, 43(8): 683-686.
    [11]
    ZHU S G, DU J P, REN N, et al. Hierarchical-Based object detection with improved locality sparse coding[J]. Chinese Journal of Electronics, 2016, 25(2): 290-295. DOI: 10.1049/cje.2016.03.015
    [12]
    温江涛, 王涛, 孙洁娣, 等. 基于深度迁移学习的复杂环境下油气管道周界入侵事件识别[J]. 仪器仪表学报, 2019, 40(8): 12-19. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201908002.htm

    WEN Jiangtao, WANG Tao, SUN Jiedi, et al. Intrusion event identification of oil and gas pipeline perimeter in complex environment based on deep migration learning[J]. Chinese Journal of Scientific Instrument, 2019, 40(8): 12-19. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201908002.htm
    [13]
    REN S, HE K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
    [14]
    LIU W, Anguelov D, Erhan D, et al. SSD: single shot multibox detector[C]//Proceedings of European Conference on Computer Vision, 2016: 21-37.
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