Mine flood perception based on gray level co-occurrence matrix and regression analysis
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摘要: 针对图像识别用于矿井水灾感知时存在识别率低、稳定性和时效性差等问题，提出了一种基于灰度共生矩阵与回归分析的矿井水灾感知方法。计算样本图像的灰度共生矩阵，提取灰度共生矩阵的对比度、差异性、齐次性、熵、相关性、能量作为特征值并组成特征向量；以样本图像的特征向量到非线性回归方程的最小距离之和最大为依据确定分类器，通过分类器识别水灾。实验结果表明，对于分辨率为256×256的图像，该方法在无烟煤、砂岩、突涌水组成的数据集上的识别率为96.33%，单张图像平均耗时16.288 5 ms。Abstract: Aiming at problems of low recognition rate and poor stability and timeliness when image recognition was used in mine flood perception, a mine flood perception method based on gray level co-occurrence matrix and regression analysis was proposed. Gray co-occurrence matrix of sample image is calculated, and contrast, dissimilarity, homogeneity, entropy, correlation and energy of the gray co-occurrence matrix are extracted as eigenvalues to form eigenvectors. The classifier is determined based on the sum of the minimum distance from the eigenvector of the sample image to nonlinear regression equation, and flood is identified by the classifier. The experimental results show that recognition rate of the method on data set composed of anthracite, sandstone and surging water is 96.33% for image with resolution of 256×256, and average time-consuming of single image is 16.288 5 ms.
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