摘要
针对钻孔返渣无法自动识别煤岩的问题,分析了煤岩识别方法和特征选择,利用提取明度分量、去噪处理、图像增强等技术对采集的钻渣样品图像进行预处理,再利用直方图辅助灰度阈值法分析煤岩图像,并采用最大类间方差法确定的最佳阈值对煤岩图像进行分割,识别分析分割后的煤岩图像。结果表明,采用灰度阈值法能准确识别出钻孔返渣样本中的煤岩成分比例,可为智能化无人钻进煤岩识别提供借鉴。
In order to solve the problem that coal and rock can not be automatically identified through drilling slag,the coal and rock identification methods and feature selection were analyzed,and the collected images of drilling slag sample were preprocessed by using the technologies of extracting lightness component,denoising and image enhancement.Then,the coal and rock images were analyzed by histogram-aided gray threshold method,and segmented by using the best threshold determined by the OTSU method.The identification results of segmented coal and rock images show that the gray threshold method can accurately identify the proportion of coal and rock components in the drilling slag samples.
作者
孙利海
李彦明
SUNLihai;LI Yanming(State Key Laboratory of the Gas Disaster Detecting,Preventing and Emergency Controlling,Chongqing 400039,China;China Coal Technology and Engineering Group Chongqing Research Institute,Chongqing 400039,China)
出处
《矿业研究与开发》
CAS
北大核心
2021年第3期113-116,共4页
Mining Research and Development
基金
国家重点研发计划项目(2018YFC0808000).
关键词
钻孔施工
煤岩图像识别
灰度阈值
无人钻进
Drilling construction
Image identification of coal and rock
Gray threshold
Unmanned drilling
