A method for detecting dust particles in excavation working face based on image analysis
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摘要: 基于光散射原理测定粉尘质量浓度只能定时定点手动检测，实时性差，且只能检测出粉尘质量浓度，并不能给出粒径分布范围。目前基于图像分析的粉尘颗粒检测研究主要是针对粉尘质量浓度或粒径分布进行单方面研究，并不能实现粉尘质量浓度和粒径分布范围的同时检测。针对上述问题，提出了一种基于图像分析的掘进工作面粉尘颗粒检测方法，探究图像特征与粉尘质量浓度、粒径分布间的关系。通过粉尘样本收集及图像采集装置，采集粉尘颗粒图像并获取采集图像时的粉尘质量浓度。编写粉尘样本图像处理算法，提取图像的灰度特征、纹理特征、几何特征相关参数。对提取的图像特征与实测粉尘质量浓度进行相关性分析，选取相关性较大的图像特征作为参数建立回归数学模型。提取粉尘颗粒对象像素点个数，结合转换系数，基于几何当量等效面积径计算粉尘粒径大小及分布范围。实验结果表明：实测粉尘质量浓度与建立的图像特征多元非线性回归模型数学模型计算值间的平均相对误差为12.37%，标准实测粒径与几何当量等效面积径得到的粒径分布间的最大相对误差为8.63%，平均相对误差为6.37%，验证了基于图像特征的粉尘质量浓度回归数学模型和基于几何当量等效面积径分布数学模型的准确性。Abstract: Based on the principle of light scattering, measuring dust concentration can only be done manually at fixed times and locations, with poor real-time performance. It can only detect dust mass concentration and cannot provide a range of particle size distribution. At present, research on dust particle detection based on image analysis mainly focuses on unilateral research on dust mass concentration or particle size distribution. It cannot achieve simultaneous detection of dust mass concentration and particle size distribution range. In order to solve the above problems, a method for detecting dust particles in excavation working face based on image analysis is proposed. It explores the relationship between image features and dust mass concentration and particle size distribution. By using a dust sample collection and image acquisition device, dust particle images are collected and the dust mass concentration at the time of image acquisition is obtained. An image processing algorithm for dust samples, is developed to extract parameters related to grayscale features, texture features, and geometric features of the image. The correlation analysis between the extracted image features and the measured dust mass concentration is performed, and the image features with high correlation is selected as parameters to establish a regression mathematical model. The method extracts the number of pixels in the dust particle object. Combining with the conversion coefficient, the method calculates the size and distribution range of the dust particle based on the geometric equivalent area diameter. The experimental results show that the average relative error between the measured dust mass concentration and the calculated values of the established image feature multiple nonlinear regression model mathematical model is 12.37%. The maximum relative error between the standard measured particle size and the geometric equivalent area size obtained from the particle size distribution is 8.63%, and the average relative error is 6.37%. This verifies the accuracy of the image feature based dust mass concentration regression mathematical model and the geometric equivalent area diameter distribution mathematical model.
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图 9 粉尘样本像素数量与整体图像像素数量的比值特征曲线
Figure 9. Ratio feature curve between the number of pixels in dust samples and the number of pixels in the overall image
图 10 图像采集时实测粉尘质量浓度
Figure 10. Actual dust concentration at the time of image acquisition
图 11 粉尘样本的粒径区间分布及占比
Figure 11. Distribution and proportion of particle size intervals in dust samples
图 12 粉尘质量浓度及粒径分布实验平台
Figure 12. Dust concentration and particle size distribution test platform
图 13 实测粒径分布与图像分析对比
Figure 13. Comparison between measured dust concentration and image analysis
表 1 特征参数与实测粉尘质量浓度
Table 1. Feature parameters and actual dust concentration
图像 灰度均值 粉尘像素数量与整体图像像素数量的比值/% 角二阶矩 纹理相关性 纹理同质性 纹理对比度 实测粉尘质量浓度/（mg·m−3） 1 129.294 22.41 0.00807 0.92941 0.42040 7.06547 281.25 2 166.347 5.87 0.02956 0.83449 0.50308 4.22411 103.84 3 195.747 2.68 0.02984 0.78039 0.48837 4.91054 79.33 $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ 98 157.779 10.76 0.00306 0.91502 0.45065 7.03906 146.87 99 160.324 11.63 0.00308 0.94126 0.40095 6.76993 154.86 100 151.017 9.17 0.00500 0.89669 0.39011 7.41074 136.24 
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表 2 实测粉尘质量浓度与特征参数相关性
Table 2. Correlation between the measured dust mass concentration and the feature parameters
特征参量 相关系数 灰度均值 −0.803 83 粉尘像素数量与整体图像像素数量的比值 0.962 32 角二阶矩 −0.298 47 纹理相关性 0.605 03 纹理同质性 −0.379 32 纹理对比度 0.580 04
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表 3 回归模型方差分析结果
Table 3. Variance analysis results of regression models
模型 F 值 P 值 R/% 多元线性模型 767.60 5.29×10−19 97.00 多元非线性模型 862.31 3.27×10−56 99.30
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表 4 粉尘实测质量浓度与模型计算浓度对比
Table 4. Comparison between the actual concentration of dust and the calculated concentration of the model
实验序号 实测粉尘质量
浓度/（mg·m−3）计算粉尘质量
浓度/（mg·m−3）误差/% 1 281.25 314.93 2.28 2 103.84 114.45 12.73 3 79.33 83.09 3.68 $\vdots $ $\vdots $ $\vdots $ $\vdots $ 98 146.87 172.60 7.79 99 154.86 170.93 6.99 100 136.24 156.20 7.52
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