摘要
为了提高X射线煤矸智能识别过程中机器对成像后图片矿物区域信息提取的精度,先选取煤和矸石中石墨、石英、高岭土、蒙脱石四种主要矿物,分别在X射线下成像,得到高能区、低能区成像图片,将图像转化为灰度图像,用迭代式阈值分割、大津阈值分割(OTSU阈值分割)、全局阈值分割、最大熵阈值分割、交叉熵阈值分割算法对图像进行分割,同时用人工分割方法获取标准分割图像,以此图像作为评价分割精度的标准。采用DICE、RVD、VOE三种标准评价指标来评价五种算法的分割效果,根据每种算法的评价指标,通过数据分析得到分割精度最高的算法,再用煤和矸石做验证试验。结果表明:最大熵阈值分割算法能够成功识别并且提取出目标矿物,效果最好,交叉熵阈值分割算法效果最差,迭代式阈值分割算法效果仅次于最大熵阈值分割算法,OTSU阈值分割算法和全局阈值分割算法分割效果次于迭代式阈值分割算法优于交叉熵阈值分割算法;煤和矸石阈值分割结果与各矿物阈值分割结果趋于一致。研究结果对后续智能分选提供了良好的预处理条件。
In order to improve accuracy of information of mineral regions from the images generated in intelligent X-ray refuse identification process,the X-ray images of graphite,quartz,kaolinite and mortmorillonite,four kinds of main minerals in coal and refuse,are respectively obtained first,and then the high-energy and low-energy X-ray images of coal and refuse are respectively produced.The images after being converted to grey-level ones are segmented using different methods including interactive threshold segmentation(ITS),OTSU threshold segmentation(OTS-TS),global threshold segmentation(GTS),maximum entropy threshold segmentation(METS)and cross entropy threshold segmentation(CETS).The artificially segmented images serve as the criteria for evaluating the accuracy of segmentations made with above-going techniques.The segmented images are processed with DICE,RVD and VOE standard evaluation algorithms to obtain the evaluation indices of each algorithm.The algorithm with the highest segmentation accuracy obtained through data analysis is then verified with coal and refuse.As evidenced by test result,the use of the METS algorithm can produce the best result and the refuse can be successfully indetified and extracted while the CETS algorithm is the least effective one among the others;in terms of accuracy and the effects that can be obtained,the ITS algorithm is only inferior to the METS algorithm while the OTSU-TS and GTS algorithms stand next only to the ITS algorithm and is better than the CES algorithm;and the results of threshold segmentation of coal and refuse are respectively consistent basically with that of each mineral.The study result provides a good coal pretreatment condition for subsequent intelligent coal cleaning operations.
作者
尹建强
朱金波
曾秋予
杨晨光
张勇
史苘桧
YIN Jianqiang;ZHU Jinbo;ZENG Qiuyu;YANG Chenguang;ZHANG Yong;SHI Qinghui(School of Materials Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China)
出处
《选煤技术》
CAS
2021年第4期24-29,共6页
Coal Preparation Technology
基金
国家重点研发计划资助项目(2019YFC1904304)
安徽省科技重大专项资助项目(18030901049)
国家重点实验室开放基金资助项目(SKLMRDPC19KF11)。
关键词
煤矸石分选
煤矸识别
X射线
灰度阈值
分割精度
separation of refuse
refuse identification
X-ray
grey-level threshold
segmentation accuracy
