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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.Abstract: At present, the concentration of pollutants in the air is detected mainly by a variety of precision testing instruments. Because of the high cost of instruments, the state set up monitoring stations in every city to detect air quality, which is coarse-grained and cannot cover every corner of the city. This paper presents an air quality detection method based on images taken by mobile devices. Now the popularity of mobile devices makes it possible for the fine-grained detection of the air quality through images. Our method carries on the analysis of color channel information entropy and gray channel information entropy, which can reflect air quality, and then we build the air quality grade detection model based on these image features. The proposed method is compared with other existing methods on our air quality image database from the estimation accuracy and the time consumption, and the experimental results show that our method is more suitable for air quality grade detection.
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Key words:
- Air quality grade detection /
- air quality image database /
- fine-grained detection /
- local information entropy
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 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 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 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
表 1 空气质量图像库
Table 1 Air quality image dataset
空气质量等级 优 良 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 
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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
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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
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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
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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
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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
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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 Ⅰ 21 iphone6s等 Ⅱ 326 未知 Ⅲ 247 OPPO R7s
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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) Ⅱ 0.6609 0.6827 0.6460 0.8497 Ⅲ 0.6411 0.6762 0.6032 0.8543
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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
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