摘要
针对钨矿石初选环节中人工手选作业效率低、成本消耗大等问题,提出机器视觉与图像处理技术相结合实时钨矿初选方法。通过引入GPU加速混合高斯模型进行矿石运动目标检测,提取图像前景中完整的矿石目标。结合图像信息,提出融合灰度特征与图像窄带的矿石目标识别算法,快速获取钨矿石中脉石的位置信息,为钨矿石的初步分选提供依据。试验结果表明,相比较传统人工手选作业方式,该方法极大提升矿石分选速度与精度,满足工业化实时检测识别要求。
Under the view of the low efficiency and high cost of manual hand separation in the primary separation of tungsten ore, a method of combining the machine vision and image processing technology to the real- time tungsten ore primary separation is proposed. By introducing GPU-accelerated mixed Gaussian model for detection of moving ore objects, the complete ore target in the image foreground is extracted. Combining with the gray character information of the image, an algorithm of ore target recognition combining gray feature and narrow band is proposed. The position information of gangue in the tungsten ore is quickly obtained, which provides the basis for the preliminary separation of tungsten ore. The experimental results show that compared with the traditional manual hand separation mode, the method can greatly improve the speed and accuracy of ore separation, and meets the requirements of real-time detection and identification in industrial applications.
作者
郭宇
张国英
孟航
GUO Yu, ZHANG Guoying, MENG Hang(School of Mechanical and Electrical Engineering, China University of Mining and Technology, Beijing 100083, Chin)
出处
《有色金属（选矿部分）》
CAS
北大核心
2018年第2期62-67,86,共7页
Nonferrous Metals（Mineral Processing Section）
关键词
混合高斯模型
钨矿初选
机器视觉
目标检测
Gaussian mixture model
tungsten ore primary separation
machine vision
target detection
