煤炭工程 ›› 2021, Vol. 53 ›› Issue (1): 160-165.doi: 10.11799/ce202101033

• 研究探讨 • 上一篇    下一篇

基于机器视觉的运动煤颗粒检测和遮挡追踪

朱振天,兰媛,胡天恩,唐建,乔葳,牛蔺楷,王杰栋   

  1. 山西省太原市太原理工大学
  • 收稿日期:2020-01-17 修回日期:2020-03-22 出版日期:2021-01-20 发布日期:2021-04-27
  • 通讯作者: 兰媛 E-mail:lanyuan@tyut.edu.cn

Moving coal particle detection and occlusion tracking based on machine vision

  • Received:2020-01-17 Revised:2020-03-22 Online:2021-01-20 Published:2021-04-27

摘要: 针对煤颗粒筛分过程中被遮挡而导致其运动分析实测数据缺失的情况,应用机器视觉的方法实现了对运动煤颗粒检测和遮挡追踪。文章搭建模拟激振实验台,使用高速摄像机采集单个煤颗粒被遮挡情况下的序列图像。在MATLAB中采用引导滤波对序列图像进行去除噪声预处理,使用混合高斯模型法(GMM)有效提取了目标煤颗粒的前景掩码,并利用卡尔曼滤波器(Kalman Filter)对目标煤颗粒进行追踪。实验结果表明,文章使用的方法能有效检测出运动煤颗粒,且对运动煤颗粒被遮挡的情况,仍具有较好的追踪鲁棒性,得到煤颗粒运动过程中的形心位置信息,为煤颗粒运动的理论分析和数值模拟的相关研究提供实验验证的基础。

关键词: 运动煤颗粒, 遮挡, 检测, 追踪

Abstract: In view of the situation that the actual data of coal particle motion analysis is missing due to being blocked in the process of coal particle screening, the method of machine vision is used to detect and track the moving coal particle. In this paper, a simulation excitation experiment platform is set up to collect the sequence image of single coal particle under occlusion by high-speed camera. In the MATLAB, the guided filter is used to remove the noise from the sequential images, and the GMM is used to extract the potential masks of the target coal particles effectively. The target coal particles are tracked by the Kalman Filter. The experimental results show that the method used in this paper can effectively detect the moving coal particles, and still has good tracking robustness for the situation that the moving coal particles are blocked. In addition, we could obtain the centroid position information in the process of coal particle movement, which provides the experimental basis for theoretical analysis and numerical simulation of coal particle movement.