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显著性引导的低光照人脸检测

李可夫 钟汇才 高兴宇 翁超群 陈振宇 李勇周 王师峥

李可夫, 钟汇才, 高兴宇, 等 . 显著性引导的低光照人脸检测[J]. 北京航空航天大学学报, 2021, 47(3): 572-584. doi: 10.13700/j.bh.1001-5965.2020.0469
引用本文: 李可夫, 钟汇才, 高兴宇, 等 . 显著性引导的低光照人脸检测[J]. 北京航空航天大学学报, 2021, 47(3): 572-584. doi: 10.13700/j.bh.1001-5965.2020.0469
LI Kefu, ZHONG Huicai, GAO Xingyu, et al. Saliency guided low-light face detection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 572-584. doi: 10.13700/j.bh.1001-5965.2020.0469(in Chinese)
Citation: LI Kefu, ZHONG Huicai, GAO Xingyu, et al. Saliency guided low-light face detection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 572-584. doi: 10.13700/j.bh.1001-5965.2020.0469(in Chinese)

显著性引导的低光照人脸检测

doi: 10.13700/j.bh.1001-5965.2020.0469
基金项目: 

国家自然科学基金 61702491

国家自然科学基金 61802390

北京市自然科学基金 4194095

茂名市科技计划 2020028

详细信息
    作者简介:

    李可夫  男,硕士研究生。主要研究方向:人工智能、计算机视觉

    钟汇才  男,博士,研究员。主要研究方向:人工智能

    高兴宇  男,博士,研究员。主要研究方向:人工智能

    通讯作者:

    高兴宇, E-mail: gaoxingyu@ime.ac.cn

  • 中图分类号: TP391

Saliency guided low-light face detection

Funds: 

National Natural Science Foundation of China 61702491

National Natural Science Foundation of China 61802390

Beijing Natural Science Foundation 4194095

Maoming Science and Technology Plan 2020028

More Information
  • 摘要:

    针对卷积神经网络难以对低光照环境拍摄的图像进行人脸检测的问题。提出了一种将图像显著性检测算法和深度学习相结合的算法,并应用于低光照人脸检测。所提算法将图像的显著性信息与图像原始RGB通道融合,用于神经网络训练。在低光照人脸数据集DARK FACE上进行了充分的实验,结果表明:所提方法在DARK FACE数据集上获得了比当前主流人脸检测算法更好的检测精度,进而验证了所提算法的有效性。

     

  • 图 1  Retinex原理示意图

    Figure 1.  Schematic of Retinex principle

    图 2  RetinaNet网络结构

    Figure 2.  Network structure of RetinaNet

    图 3  WIDER FACE数据集图像示例

    Figure 3.  Image examples of WIDER FACE dataset

    图 4  DARK FACE数据集原图

    Figure 4.  Original images of DARK FACE dataset

    图 5  采用MSRCR算法的增强图像

    Figure 5.  Images enhanced using MSRCR algorithm

    图 6  DARK FACE数据集图像的显著图

    Figure 6.  Saliency maps of DARK FACE dataset images

    图 7  显著性引导增强的DARK FACE图像(r=0.1)

    Figure 7.  DARK FACE images enhanced using saliency guidance (r=0.1)

    图 8  显著性引导增强的DARK FACE图像(r=0.2)

    Figure 8.  DARK FACE images enhanced using saliency guidance (r=0.2)

    图 9  原图融合显著图四通道训练示意图

    Figure 9.  Schematic of training using 4 channel input fused by original images and saliency map

    图 10  查准率-召回率曲线

    Figure 10.  Precision-recall curves

    图 11  检测结果可视化

    Figure 11.  Visualization of detection results

    表  1  DARK FACE数据集发布的检测精度(使用图像增强算法)

    Table  1.   Detection accuracies published from DARK FACE dataset (with image enhancement)

    检测算法 精度
    DSFD+MF 0.414
    DSFD+MSRCR 0.413
    DSFD+LIME 0.403
    DSFD+BIMEF 0.402
    DSFD+Dehazing 0.365
    DSFD+RetinexNet 0.332
    DSFD+JED 0.179
    PyramidBox+MF 0.263
    PyramidBox+Dehazing 0.249
    PyramidBox+LIME 0.248
    PyramidBox+MSRCR 0.246
    PyramidBox+BIMEF 0.245
    PyramidBox+RetinexNet 0.207
    PyramidBox+JED 0.146
    下载: 导出CSV

    表  2  DARK FACE数据集发布的检测精度(未使用图像增强算法)

    Table  2.   Detection accuracies published from DARK FACE dataset (without image enhancement)

    检测算法 精度
    DSFD 0.153
    Faster R-CNN 0.017
    PyramidBox 0.132
    SSH 0.076
    下载: 导出CSV

    表  3  CVPR 2019 UG2+国际竞赛发布的基准检测精度(使用图像增强算法)

    Table  3.   Benchmark accuracies published from CVPR 2019 UG2+ competition (with image enhancement)

    检测算法 精度
    DSFD+MF 0.393
    DSFD+MSRCR 0.393
    DSFD+BIMEF 0.383
    DSFD+LIME 0.383
    DSFD+Dehazing 0.348
    DSFD+RetinexNet 0.316
    DSFD+JED 0.170
    PyramidBox+MF 0.251
    PyramidBox+Dehazing 0.237
    PyramidBox+LIME 0.237
    PyramidBox+MSRCR 0.235
    PyramidBox+BIMEF 0.234
    PyramidBox+RetinexNet 0.199
    PyramidBox+JED 0.138
    下载: 导出CSV

    表  4  CVPR 2019 UG2+国际竞赛发布的基准检测精度(未使用图像增强算法)

    Table  4.   Benchmark accuracies published from CVPR 2019 UG2+ competition (without image enhancement)

    检测算法 精度
    DSFD 0.136
    Faster R-CNN 0.125
    PyramidBox 0.069
    SSH 0.017
    下载: 导出CSV

    表  5  本文实验模型检测精度

    Table  5.   Detection accuracy of experimental models from this paper

    训练方法 精度
    DARK FACE数据集原图训练 0.504
    MSRCR增强DARK FACE训练 0.522
    显著性引导增强DARK FACE训练(r=0.1) 0.540
    显著性引导增强DARK FACE训练(r=0.2) 0.533
    DARK FACE原图融合显著图四通道训练 0.560
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-27
  • 录用日期:  2020-10-21
  • 刊出日期:  2021-03-20

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