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一种复杂场景下的人眼检测算法

崔家礼 曹衡 张亚明 罗嗣梧 李锦涛 王华峰

崔家礼, 曹衡, 张亚明, 等 . 一种复杂场景下的人眼检测算法[J]. 北京航空航天大学学报, 2021, 47(1): 38-44. doi: 10.13700/j.bh.1001-5965.2019.0641
引用本文: 崔家礼, 曹衡, 张亚明, 等 . 一种复杂场景下的人眼检测算法[J]. 北京航空航天大学学报, 2021, 47(1): 38-44. doi: 10.13700/j.bh.1001-5965.2019.0641
CUI Jiali, CAO Heng, ZHANG Yaming, et al. A human eye detection algorithm in complex scenarios[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(1): 38-44. doi: 10.13700/j.bh.1001-5965.2019.0641(in Chinese)
Citation: CUI Jiali, CAO Heng, ZHANG Yaming, et al. A human eye detection algorithm in complex scenarios[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(1): 38-44. doi: 10.13700/j.bh.1001-5965.2019.0641(in Chinese)

一种复杂场景下的人眼检测算法

doi: 10.13700/j.bh.1001-5965.2019.0641
基金项目: 

国家重点研发计划 2017YFB0802300

详细信息
    作者简介:

    崔家礼  男, 博士, 助理研究员。主要研究方向:智能识别与数字图像处理

    曹衡  男, 硕士研究生。主要研究方向:智能识别与数字图像处理

    王华峰  男, 博士, 副研究员。主要研究方向:人工智能与机器人

    通讯作者:

    王华峰, E-mail: wanghuafeng@ncut.edu.cn

  • 中图分类号: TP391.41;TP37

A human eye detection algorithm in complex scenarios

Funds: 

National Key R & D Program of China 2017YFB0802300

More Information
  • 摘要:

    针对复杂场景下的人眼检测问题,间接方法和直接方法具有一定的局限性。提出了一种不依赖人脸检测的直接型人眼检测算法,以解决复杂场景下多尺度尤其是小尺度人眼检测问题。算法通过减少下采样因子并加入扩张残差单元以提升小尺度人眼检测能力,且对多尺度特征相互拼接以保证多尺度人眼检测的精度。同时,算法借助于压缩特征输出通道降低了模型复杂度,使人眼检测效率得以提升。实验结果表明:所提模型可以在小尺度下有效地进行左右眼区分,并在红外数据上表现良好。经在DIF数据集上进行训练与测试,所提模型在较小尺度下人眼检测精度达到82.59%,检测效率达到30.5 fps。

     

  • 图 1  本文算法网络架构

    Figure 1.  Proposed algorithmic network architecture

    图 2  两种改进的扩张残差单元

    Figure 2.  Two improved expanded residual units

    图 3  骨干网络下采样

    Figure 3.  Backbone network downsampling

    图 4  人眼数据集实例

    Figure 4.  Examples of human eye datasets

    图 5  眼睛区域不可见及过度模糊等极端情况

    Figure 5.  Extreme cases such as invisible eye area and excessive blur

    图 6  CASIA-Iris-Distance数据集中的红外图像

    Figure 6.  Infrared images in CASIA-Iris-Distance dataset

    图 7  平均交并比与锚点框数量之间的关系

    Figure 7.  Relationship between average IoU and number of anchor boxes

    图 8  不同算法在不同尺度下的检出率

    Figure 8.  Statistics of detection rate of different algorithms at different scales

    图 9  四种算法的检测结果对比

    Figure 9.  Comparison of detection results among four algorithms

    图 10  红外图像的人眼检测结果

    Figure 10.  Human eye detection results of infrared image

    表  1  不同算法优劣势比较

    Table  1.   Comparison of advantages and disadvantages among different algorithms

    算法 劣势 优势
    YOLOv3 小尺度目标检测效果较差 检测效率高
    Faster R-CNN 实时性差,小尺度目标检测效果差 检测精度较高
    Adaboost 检测精度低,小尺度目标检测效果很差 检测效率高
    下载: 导出CSV

    表  2  不同尺度网络在DIF数据集上的实验结果

    Table  2.   Experimental results of different scale networks on DIF dataset

    算法 尺度数 特征图 输入 mAP/% F/fps
    YOLOv3 3 52×26×13 416×416 76.97 33.4
    Proposed-3 3 26×26×26 416×416 80.5 31.8
    Proposed-4 4 52×26×26×26 416×416 82.4 30.5
    Proposed-5 5 104×52×26×26×26 416×416 82.68 27.3
    注:fps为帧/s。
    下载: 导出CSV

    表  3  人眼检测统计结果

    Table  3.   Human eye detection statistic results

    场景 左眼检出率/% 左眼漏检率/% 右眼检出率/% 右眼漏检率/% 误检率/%
    A 85.9 14.1 89.1 10.9 21.4
    B 83.3 16.7 86.1 13.9 19.4
    下载: 导出CSV

    表  4  不同算法的评价指标对比

    Table  4.   Comparison of evaluation indicators among various algorithms

    算法 AP_L/% AP_R/% mAP/% F/fps
    YOLOv3 76.71 79.23 77.97 33.4
    YOLOv3-tiny 74.66 75.3 74.98 204.3
    YOLOv2 60.97 65.45 63.21 45.6
    Faster R-CNN 77.91 79.35 78.63 12
    Proposed 84.77 80.41 82.59 30.5
    下载: 导出CSV

    表  5  红外人眼检测性能

    Table  5.   Performance of infrared human eye detection

    算法 AP_L/% AP_R/% mAP/% F/fps
    Proposed 91.82 94.48 93.15 31.2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-23
  • 录用日期:  2020-04-03
  • 刊出日期:  2021-01-20

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