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基于图像的空气质量等级检测

杨本芊 徐琳 陈强

杨本芊, 徐琳, 陈强. 基于图像的空气质量等级检测. 自动化学报, 2020, 46(11): 2404−2416 doi: 10.16383/j.aas.c180041
引用本文: 杨本芊, 徐琳, 陈强. 基于图像的空气质量等级检测. 自动化学报, 2020, 46(11): 2404−2416 doi: 10.16383/j.aas.c180041
Yang Ben-Qian, Xu Lin, Chen Qiang. Air quality grade detection based on image. Acta Automatica Sinica, 2020, 46(11): 2404−2416 doi: 10.16383/j.aas.c180041
Citation: Yang Ben-Qian, Xu Lin, Chen Qiang. Air quality grade detection based on image. Acta Automatica Sinica, 2020, 46(11): 2404−2416 doi: 10.16383/j.aas.c180041

基于图像的空气质量等级检测

doi: 10.16383/j.aas.c180041
基金项目: 

国家自然科学基金 61671242

国家自然科学基金 61501522

详细信息
    作者简介:

    杨本芊  南京理工大学计算机科学与工程学院硕士研究生.主要研究方向为图像处理, 机器学习, 图像质量与空气质量相关性分析. E-mail: yangbenqian 7@163.com

    徐琳  南京理工大学计算机科学与工程学院硕士研究生.主要研究方向为图像处理. E-mail: 13813376047@163.com

    通讯作者:

    陈强  南京理工大学计算机科学与工程学院教授.主要研究方向为图像处理和分析.本文通信作者. E-mail: chen2qiang@njust.edu.cn

  • 本文责任编委  金连文

Air Quality Grade Detection Based on Image

Funds: 

National Natural Science Foundation of China 61671242

National Natural Science Foundation of China 61501522

More Information
    Author Bio:

    YANG Ben-Qian   Master student at the School of Computer Science and Engineering, Nanjing University of Science and Technology. Her research interest covers image processing, machine learning, and correlation analysis between image quality and air quality

    XU Lin   Master student at the School of Computer Science and Technology, Nanjing University of Science and Technology. Her main research interest is image processing

    Corresponding author: CHEN Qiang   Professor at the School of Computer Science and Engineering, Nanjing University of Science and Technology. His research interest covers image processing and analysis. Corresponding author of this paper
  • Recommended by Associate Editor JIN Lian-Wen
  • 摘要: 目前国内主要依靠各种精密仪器检测空气中的污染物浓度.由于仪器的成本较高, 国家通过在每个城市设立监测站来检测空气质量, 这种空气质量检测方法是粗粒度的, 不能覆盖城市的每个角落.本文提出了一种基于图像的空气质量等级检测方法, 旨在通过移动设备采集的图像检测空气质量等级, 移动设备的普及使得通过图像细粒度检测空气质量成为可能, 该方法利用空气污染对图像颜色通道和灰度通道局部信息熵的影响构建空气质量等级检测模型.在本文构建的空气质量图像库进行了模型测试和比较分析, 实验结果表明:本文方法能够准确地评估空气质量等级, 比其他已有相关方法更适用于空气质量等级检测.
    Recommended by Associate Editor JIN Lian-Wen
    1)  本文责任编委  金连文
  • 图  1  基于传统机器学习的空气质量估计模型构建流程

    Fig.  1  Air quality estimation model construction process based on traditional machine learning

    图  2  空气质量图像库中不同等级图像示例

    Fig.  2  Examples of images with different grades in the air quality image dataset

    图  3  空气污染对图像灰度通道和彩色通道的影响

    Fig.  3  The effect of air pollution on the gray and color channels of images

    图  4  不同空气质量等级下图像示意图

    Fig.  4  Examples of images at different air quality grades

    图  5  不同空气质量等级图像局部信息熵归一化直方图

    Fig.  5  Histogram of normalized local information entropy for images at different air quality grades

    图  6  不同失真效果图

    Fig.  6  Images under different kinds of distortion

    图  7  原图和各种失真图像局部信息熵归一化直方图

    Fig.  7  Histogram of normalized local information entropy for the original image and images with different kinds of distortions

    图  8  不同空气质量等级在局部信息熵均值和斜率两个特征上的分布

    Fig.  8  The average and slope distribution of the local information entropy with different air quality grades

    图  9  特征提取流程图

    Fig.  9  The flow chart of feature extraction

    图  10  基于图像的空气质量等级检测方法流程图

    Fig.  10  The flow chart of the image based air quality grade detection method

    图  11  数据集I测试图像及测试结果

    Fig.  11  The testing images and output results on Dataset I

    图  12  数据集Ⅱ样例图

    Fig.  12  Samples of Dataset Ⅱ

    图  13  数据集Ⅲ GOD直方图分布

    Fig.  13  The GOD histogram distribution on Dataset Ⅲ

    图  14  不适用图像示意图

    Fig.  14  Examples of inapplicable images

    表  1  空气质量图像库

    Table  1  Air quality image dataset

    空气质量等级 轻度污染 中度污染 重度污染 严重污染
    图像数量(幅) 15 13 15 16 16 25
    下载: 导出CSV

    表  2  基于图像检测空气质量等级通用模型所用特征总结

    Table  2  The summary of features used in the general model of air quality grade detection based on images

    特征序号 特征意义
    f1$ \sim $f2 灰度通道空间域局部信息熵均值和斜率
    f3$ \sim $f4 灰度通道频域局部信息熵均值和斜率
    f5$ \sim $f6 颜色通道频域局部信息熵均值和斜率
    下载: 导出CSV

    表  3  六种方法性能对比

    Table  3  The performance comparison of the six methods

    方法 $mean_{\rm LCC}$ $mean_{\rm SROCC}$ TIME (s) $var_{\rm LCC}$ $var_{\rm SROCC}$ $mean_{acc(0)}$ $mean_{acc(1)}$
    SSEQ [24] 0.8400 0.8384 1 345.3 0.0024 0.0035 0.7318 0.9400
    IQALE-a [25] 0.8283 0.7770 1 892.1 0.0038 0.0067 0.7342 0.9235
    IQALE-b [25] 0.8195 0.7791 1 912.6 0.0048 0.0075 0.7127 0.9336
    IQALE-a, b [25] 0.8142 0.7618 2 828.0 0.0041 0.0076 0.7218 0.9183
    Chen等[21] 0.84372 0.8145 26 485.1 0.0037 0.0049 0.7370 0.9450
    GIST [27] 0.8300 0.8136 32.69 0.0018 0.0021 0.7099 0.9344
    Alexnet [28] 0.8807 0.8743 16 290.0 0.0015 0.0019 0.6818 0.9250
    Our-a 0.9002 0.8598 966.9 0.0011 0.0023 0.8110 0.9728
    Our-b 0.8915 0.8595 971.5 0.0016 0.0028 0.7954 0.9730
    Our-a, b 0.8993 0.8596 1 343.8 0.0010 0.0025 0.8126 0.9744
    下载: 导出CSV

    表  4  局部块大小对方法性能的影响

    Table  4  The influence of the size of the local block on our method

    局部块大小 $mean_{\rm LCC}$ $mean_{\rm SROCC}$ TIME (s) $var_{\rm LCC}$ $var_{\rm SROCC}$ $mean_{acc(0)}$ $mean_{acc(1)}$
    $2\times 2$ 0.8662 0.8818 15 071.33 0.0008 0.0008 0.7074 0.9671
    $4\times 4$ 0.9093 0.8911 3 661.60 0.0005 0.0009 0.8150 0.9769
    $8\times 8$ 0.9002 0.8598 966.92 0.0011 0.0023 0.8110 0.9728
    $16\times 16$ 0.8865 0.8524 310.18 0.0018 0.0029 0.7955 0.9682
    $32\times 32$ 0.8551 0.8381 128.99 0.0035 0.0048 0.7670 0.9508
    下载: 导出CSV

    表  5  局部熵百分比对方法性能的影响

    Table  5  The influence of the percentage of the local entropy on our method

    局部熵百分比 $mean_{\rm LCC}$ $mean_{\rm SROCC}$ TIME (s) $var_{\rm LCC}$ $var_{\rm SROCC}$ $mean_{acc(0)}$ $mean_{acc(1)}$
    100 % 0.9002 0.8598 966.92 0.0011 0.0023 0.8110 0.9728
    90 % 0.8930 0.8635 952.17 0.0013 0.0027 0.8116 0.9697
    80 % 0.8889 0.8585 948.09 0.0015 0.0027 0.8073 0.9665
    70 % 0.8864 0.8528 945.01 0.0014 0.0029 0.8001 0.9636
    60 % 0.8834 0.8443 938.72 0.0013 0.0028 0.7974 0.9613
    下载: 导出CSV

    表  6  尺度个数对方法性能的影响

    Table  6  The influence of the number of scales on our method

    尺度 $mean_{\rm LCC}$ $mean_{\rm SROCC}$ TIME (s) $var_{\rm LCC}$ $var_{\rm SROCC}$ $mean_{acc(0)}$ $mean_{acc(1)}$
    1 0.9002 0.8598 966.92 0.0011 0.0023 0.8110 0.9728
    2 0.8810 0.8348 1272.96 0.0020 0.0041 0.7934 0.9656
    3 0.8646 0.8124 1363.92 0.0027 0.0057 0.7729 0.9511
    4 0.8390 0.7857 1393.75 0.0027 0.0054 0.7482 0.9313
    下载: 导出CSV

    表  7  适用性实验数据集

    Table  7  Applicability experiment datasets

    数据集 图像数量(幅) 采集设备
    21 iphone6s等
    326 未知
    247 OPPO R7s
    下载: 导出CSV

    表  8  本文方法在数据集Ⅱ和数据集Ⅲ上的测试结果

    Table  8  Testing results of the proposed method on Dataset Ⅱ and Dataset Ⅲ

    数据集 LCC SROCC ACC(0) ACC(1)
    0.6609 0.6827 0.6460 0.8497
    0.6411 0.6762 0.6032 0.8543
    下载: 导出CSV

    表  9  本文方法在空气质量图像库上的测试结果

    Table  9  The testing results of the proposed method on the air quality image dataset

    数据集 LCC SROCC ACC(0) ACC(1)
    空气质量图像库 0.8721 0.8872 0.6700 0.9300
    下载: 导出CSV
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  • 收稿日期:  2018-01-17
  • 录用日期:  2018-07-02
  • 刊出日期:  2020-11-24

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