ABSTRACT

To solve the problem of online detection of coal-carrying rate in gangue, an approach based on particle queuing and binocular machine vision was proposed. First, a queuing device was applied to line up the coal and gangue particles. A binocular camera was used to obtain images of coal and gangue from above the belt. Through image segmentation, the coal and gangue particle regions were divided from the belt background. Then, the Semi-Global Block Matching (SGBM) algorithm was used to obtain the height information matrix of each region. At the same time, the projected area of the particles was calculated from the area of the divided regions. It was converted from pixel units to physical units by capturing an image of a ruler. The height matrix and projected area were combined to construct a mechanism-based volume model by integral principle. Finally, the calculated volume of coal and gangue were multiplied by their respective empirical density to get the mass of particles. The coal-carrying rate in gangue was obtained by dividing the mass of coal by the total mass. The average relative error of the proposed method is 2.79%. It not only overcomes the prediction error caused by particle stacking, but also improves the computational efficiency of image processing, and the “white-box model” is also easy to understand.

摘要

针对煤矸石含煤率在线检测问题，提出了一种基于粒子排队和双目机器视觉的煤矸石含煤率在线检测方法。首先，采用排队装置将煤和煤矸石颗粒排列起来。使用双目相机从传送带上方获取煤炭和煤矸石的图像。通过图像分割，将煤和煤矸石颗粒区域从带背景中划分出来。然后，使用半全局块匹配（SGBM）算法获得每个区域的高度信息矩阵。同时，根据分割区域的面积计算颗粒的投影面积。通过捕获尺子的图像，将其从像素单位转换为物理单位。将高度矩阵和投影面积结合起来，利用积分原理构建基于机构的体积模型。最后，将计算出的煤和矸石的体积乘以各自的经验密度即可得到颗粒的质量。煤矸石的含煤率是用煤的质量除以总质量得到的。该方法的平均相对误差为2.79%。它不仅克服了粒子堆叠带来的预测误差，还提高了图像处理的计算效率，而且“白盒模型”也易于理解。