基于钻孔返渣图像的煤岩界面识别方法研究

Research on coal-rock interface recognition method based on returned slag image of borehole

为满足未来煤矿井下钻孔机器人对智能化施工的要求,针对钻孔返渣中只有岩屑和煤渣,且二者颜色特征差异大的特点,利用返渣图像特征进行了煤岩识别技术研究。结果表明:研究的煤岩界面识别方法主要包括图像预处理、阈值分割和图像识别3个步骤;其中图像预处理先采用HSV颜色空间进行转换以提取明度分量,再采用高斯滤波进行图像去噪,最后采用拉普拉斯方法实现图像增强;将预处理图像采用固定单阈值分割方案进行图像分割,并以实验采集数据为例,利用最大类间方差法确定了采集样品图像的固定阈值为115;最后在图像分割的二值图像基础上,通过计算煤渣和岩屑在图像像素点总数中各自所占比例,进行煤岩标识,再通过分析场景中采集的大量样本数,设置不同煤层条件下煤、岩界限的阈值,从而实现了图像识别。

In order to meet the requirements of intelligent construction for borehole robots in the future, the coal and rock identification technology is studied by using the image characteristics of the returned slag, which is characterized by only cuttings and cinders in the returned slag. The results show that the coal and rock interface recognition methods mainly include image preprocessing, threshold segmentation and image recognition. In the image preprocessing, HSV color space is first used for conversion to extract the brightness component, then Gaussian filter is used for image denoising, and finally Laplace method is used to realize image enhancement. The preprocessed image was segmented with a fixed single threshold. Taking the experimental data as an example, the fixed threshold value of the sample image was determined to be 115 by using the maximum variance method. Finally, based on the binary image of image segmentation, coal and rock were identified by calculating the proportion of coal cinder and cuttings in the total number of image pixels. Then, by analyzing the large number of samples collected in the scene, threshold values of coal and rock boundary were set under different coal seam conditions, so as to realize image recognition.