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
-
摘要: 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。Abstract: 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.
-
-
[1] 王国法,赵国瑞,胡亚辉.5G技术在煤矿智能化中的应用展望[J].煤炭学报,2020,45(1):16-23. WANG Guofa, ZHAO Guorui, HU Yahui. Application prospect of 5G technology in coal mine intelligence[J]. Journal of China Coal Society, 2020, 45(1): 16-23.
[2] 王国法,王虹,任怀伟,等.智慧煤矿2025情景目标和发展路径[J].煤炭学报,2018,43(2):295-305. WANG Guofa, WANG Hong, REN Huaiwei, et al. 2025 scenarios and development path of intelligent coal mine[J]. Journal of China Coal Society, 2018, 43(2): 295.
[3] 刘峰,曹文君,张建明.持续推进煤矿智能化 促进我国煤炭工业高质量发展[J].中国煤炭,2019,45(12):32-36. LIU Feng, CAO Wenjun, ZHANG Jianming. Continuously promoting the coal mine intellectualization and the high-quality development of China’s coal industry[J]. China Coal, 2019, 45(12): 32-36.
[4] 王国法,杜毅博.煤矿智能化标准体系框架与建设思路[J].煤炭科学技术,2020,48(1):1-9. WANG Guofa, DU Yibo. Coal mine intelligent standard system framework and construction ideas[J]. Coal Science and Technology, 2020, 48(1): 1-9.
[5] 黄韶杰.基于灰度阈值法的煤岩界面识别研究[J].工矿自动化,2013,39(5):52-54. HUANG Shaojie. Research of coal-rock interface identification based on gray threshold method[J]. Industry and Mine Automation, 2013, 39(5): 52-54.
[6] 田子建,彭霞,苏波.基于机器视觉的煤岩界面识别研究[J].工矿自动化,2013,39(5):49-52. TIAN Zijian, PENG Xia, SU Bo. Research of coal-rock interface identification based on machine vision[J]. Industry and Mine Automation, 2013, 39(5): 49-52.
[7] 黄韶杰.数字视频技术在自动化工作面的应用[J].煤炭工程,2014,46(2):136-138. HUANG Shaojie. Digital video technology applied to automatic coal mining face[J]. Coal Engineering, 2014, 46(2): 136-138.
[8] 鲁晓丽.煤岩识别方法技术分析[J].电子制作,2013(14):243. [9] 任芳,杨兆建,熊诗波.国内外煤岩界面识别技术研究动态综述[J].煤,2001,10(4):54-55. [10] 伍云霞,田一民.基于字典学习的煤岩图像特征提取与识别方法[J].煤炭学报,2016,41(12):3190-3196. WU Yunxia, TIAN Yimin. Method of coal-rock image feature extraction and recognition based on dictionary learning[J]. Journal of China Coal Society, 2016, 41(12): 3190-3196.
[11] 任芳.基于多传感器数据融合技术的煤岩界面识别的理论与方法研究[D].太原:太原理工大学,2003. [12] 翟俊海,赵文秀,王熙照.图像特征提取研究[J].河北大学学报(自然科学版),2009,29(1):106-112. ZHAI Junhai, ZHAO Wenxiu, WANG Xizhao. Research on the image feature extraction[J]. Journal of Hebei University(Natural Science Edition), 2009, 29(1): 106-112.
[13] 杨成佳.图像去噪及其效果评估若干问题研究[D].长春:吉林大学,2016. -
期刊类型引用(6)
1. 周素静，康楠，余敏. 基于变形分数阶Lorenz混沌系统的煤岩界面图像分割方法. 金属矿山. 2024(12): 246-251 . 
百度学术
2. 张新华，田加加. 松软煤层瓦斯抽采钻孔煤岩层位识别关键技术研究与应用. 能源与环保. 2023(09): 20-25 . 百度学术
3. 杨程涛，刘伟，武腾飞，熊俊杰. 基于钻进参数的钻孔煤岩界面识别系统开发及应用. 煤炭技术. 2023(12): 182-185 . 百度学术
4. 张强，张润鑫，刘峻铭，王聪，张赫哲，田莹. 煤矿智能化开采煤岩识别技术综述. 煤炭科学技术. 2022(02): 1-26 . 百度学术
5. 杨宇博，田慕琴. 基于麻雀优化的二维Otsu煤岩界面识别方法. 现代电子技术. 2022(19): 49-53 . 百度学术
6. 李彦明，孙利海. 基于多源异构信息耦合的煤岩界面识别技术研究. 矿业安全与环保. 2022(05): 6-10 . 百度学术
其他类型引用(7)
计量
- 文章访问数: 42
- HTML全文浏览量: 3
- PDF下载量: 0
- 被引次数: 13
下载:
