Research on image recognition methods for coal rock fractures
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摘要: 煤岩裂隙与瓦斯运移密切相关,且影响煤岩体稳定性,研究煤岩体中复杂的裂隙系统对于巷道支护和瓦斯抽采有重要意义。目前煤岩裂隙图像识别方法未能综合考虑煤岩图像裂隙数量、位置、形态和类别等特点,难以获取有效信息。以鹤壁煤电股份有限公司第八煤矿掘进工作面煤岩图像为研究对象,提出了一种基于U−Net网络对图像中裂隙及类别实现像素级智能识别的方法。采用直方图均衡化、高斯双边滤波和拉普拉斯算子对煤岩图像进行预处理,以提高图像质量,更有效地提取裂隙特征信息。通过观测记录煤岩裂隙特征并分为7类,对筛选出的煤岩裂隙图像进行扩增,采用Labelme软件对图像进行像素级标注,建立煤岩裂隙数据集。采用U−Net网络构建煤岩裂隙识别模型,经调试确定网络批量大小和学习率参数,实验表明当迭代次数达到300以上时,该模型的识别精确率均值为87%,召回率均值为92%,平均交并比大于85%,类别平均像素准确率大于80%。采集井下煤岩采动裂隙和实验室张性外生裂隙对煤岩裂隙识别模型进行验证,结果表明该模型可有效提取目标特征信息并与背景特征信息区分,能够较准确地定位、识别单一裂隙。Abstract: Coal rock fractures are closely related to gas migration and affect the stability of coal rock. Studying the complex fracture system in coal rock is of great significance for roadway support and gas extraction. At present, the recognition methods for coal rock fracture images fail to comprehensively consider the features of the number, position, morphology, and category of fracture in coal rock images, making it difficult to obtain effective information. Taking the coal rock images of excavation face in the No.8 Coal Mine of Hebi Coal and Electricity Co., Ltd. as the research object, a pixel level intelligent recognition method based on U-Net network for coal rock fractures and categories is proposed. The histogram equalization, Gaussian bilateral filtering, and Laplace operator are used to preprocess coal rock images to improve image quality and extract fracture feature information more effectively. The features of coal rock fractures are recorded by observing and divided into 7 categories, the selected coal rock fracture images are amplified, and the images are annotated at the pixel level using Labelme software to establish a coal rock fracture dataset. The U-Net network is used to construct a coal rock fracture recognition model. After debugging, the network batch size and learning rate parameters are determined. The experiment shows that when the number of iterations reaches 300 or more, the average recognition accuracy of the model is 87%, the average recall rate is 92%, the average intersection to parallel ratio is greater than 85%, and the average pixel accuracy of the category is greater than 80%. The coal rock fracture recognition model is validated by collecting underground coal rock mining fractures and laboratory tensile exogenous fractures. The results show that the model can effectively extract target feature information and distinguish it from background feature information, and can accurately locate and recognize a single fracture.
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表 1 煤岩裂隙数据集
Table 1. Data set of coal rock fracture
裂隙类别 训练样本个数 测试样本个数 内生裂隙 197 49 张性外生裂隙 132 33 剪性外生裂隙 75 18 张剪性外生裂隙 43 11 压剪性外生裂隙 82 21 劈理 34 9 采动裂隙 74 18 表 2 不同模型的煤岩裂隙识别准确率
Table 2. Accuracy rate of coal rock fracture identification by different models
模型 准确率/% 训练集 测试集 SegNet 64.63 55.38 DeepLab v3+ 67.41 62.86 PspNet 68.37 53.82 U−Net 71.71 66.01 表 3 煤岩裂隙识别模型网络参数
Table 3. Network parameters of coal rock fracture identification model
层次结构 参数设置 卷积层_1 卷积核数:16;卷积核尺寸:5×5×3;步长:1;全零填充
卷积核数:16;卷积核尺寸:5×5×3;步长:1;全零填充池化层_1 池化核尺寸:2×2;步长:2 卷积层_2 卷积核数:32;卷积核尺寸:3×3×3;步长:1;全零填充
卷积核数:32;卷积核尺寸:3×3×3;步长:1;全零填充池化层_2 池化核尺寸:2×2;步长:2 卷积层_3 卷积核数:64;卷积核尺寸:3×3×3;步长:1;全零填充
卷积核数:64;卷积核尺寸:3×3×3;步长:1;全零填充
卷积核数:64;卷积核尺寸:3×3×3;步长:1;全零填充上采样_4、
拼接融合上采样因子:2×2×3 卷积层_5 卷积核数:64;卷积核尺寸:3×3×3;步长:1;全零填充
卷积核数:64;卷积核尺寸:3×3×3;步长:1;全零填充上采样_6、
拼接融合上采样因子:2×2×3 卷积层_7 卷积核数:32;卷积核尺寸:3×3×3;步长:1;全零填充
卷积核数:32;卷积核尺寸:3×3×3;步长:1;全零填充卷积层_8 卷积核数:8;卷积核尺寸:1×1×3;步长:1;全零填充 表 4 煤岩裂隙识别模型训练结果
Table 4. Training results of coal rock fracture identification model
批量大小 不同学习率下的识别准确率/% 0.01 0.001 0.000 5 0.000 1 0.000 01 训练集 测试集 训练集 测试集 训练集 测试集 训练集 测试集 训练集 测试集 2 30.19 27.99 76.31 77.26 90.36 71.21 83.05 82.93 79.13 68.96 4 48.38 46.95 78.55 69.34 90.36 71.19 89.23 83.07 80.52 68.52 6 63.52 52.68 72.43 73.88 85.74 68.01 87.69 89.43 84.13 77.53 8 42.41 41.26 87.14 74.67 89.31 68.61 82.45 87.31 88.39 72.45 10 42.87 38.57 84.91 80.50 89.72 71.70 86.03 82.60 82.60 65.42 -
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