Research on high-precision coal flow detection of belt conveyors based on machine vision
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摘要: 针对现有基于机器视觉的带式输送机煤流检测方法存在的图像细节缺失、在多处断裂或断裂间距较大区域拟合效果较差的问题，基于直射斜收式激光三角测量原理，提出了一种基于机器视觉的带式输送机高精度煤流检测系统，将线激光发射器布置在带式输送机测量位置正上方并垂直照射煤堆，煤堆随带式输送机匀速运动，利用相机在斜上方实时拍摄包含激光条纹的煤堆表面图像。对煤流检测系统进行标定，包括相机内参数标定和激光平面标定，得到煤堆的高度信息；对煤流截面激光条纹图像进行处理，从提取精度、算法实时性等角度对比分析了灰度重心法和区域骨架法，根据对比结果选用区域骨架法提取激光条纹中心；针对利用图像膨胀操作进行激光条纹断裂修补拟合效果较差的问题，提出采用最小二乘法作为激光条纹断裂修补算法，相较于闭运算，最小二乘法拟合处理的平滑效果更好，精度较高；建立煤流截面积计算模型，通过计算每一帧上煤堆的横截面积，即可得出不同带速下的煤流体积。实验结果表明，当带速分别为0.25，0.5，1 m/s时，煤流检测系统误差均较小，最大误差分别为2.78%，3.61%和3.89%，验证了煤流检测系统具有较高的准确性。Abstract: In response to the problems of missing image details and poor fitting effect in multiple fractures or areas with large fracture spacing in existing machine vision based coal flow detection methods for belt conveyors, a high-precision coal flow detection system for belt conveyors based on machine vision is proposed. It is based on the principle of direct beam oblique collection laser triangulation. The line laser emitter is arranged directly above the measurement position of the belt conveyor and vertically irradiates the coal pile. The coal pile moves uniformly with the belt conveyor, and a camera at an oblique angle is used to capture real-time images of the surface of the coal pile containing laser stripes. The method calibrates the coal flow detection system, including camera internal parameter calibration and laser plane calibration, to obtain the height information of the coal pile. The processing of laser stripe images on coal flow cross-sections is carried out. The gray center of gravity method and regional skeleton method are compared and analyzed from multiple perspectives such as extraction precision and algorithm real-time performance. Based on the comparison results, the regional skeleton method is selected to extract the center of laser stripes. Aiming at the problem of poor fitting effect of laser stripe fracture repair using image dilation operation, the least squares method is proposed as the laser stripe fracture repair algorithm. Compared with closed operations, the least squares method has better smoothing effect and higher precision in fitting processing. The method establishes a coal flow cross-sectional area calculation model. By calculating the cross-sectional area of the coal pile at each frame, the coal flow volume at different belt speeds can be obtained. The experimental results show that when the belt speeds are 0.25, 0.5, and 1 m/s respectively, the detection system errors are relatively small, with maximum errors of 2.78%, 3.61%, and 3.89%. It verifies that the coal flow detection system has high accuracy.
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图 14 闭运算和最小二乘法拟合效果对比
Figure 14. Comparison between closed operations and least square method
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图 16 激光条纹像素点序号与x方向真实世界坐标对应关系
Figure 16. Correspondence between the pixel number of laser stripes and the real-world coordinates in the x direction
表 1 带式输送机相关参数
Table 1 Belt conveyor related parameters
参数 值 额定运量/（t·h−1） 15 运输距离/m 2.5 胶带宽度/mm 400 胶带速度/（m·s−1） 0～1 
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表 2 相机内参信息
Table 2 Camera internal reference information
参数 值 平均误差 0.046 038 单个像元宽度/μm 4.836 41 单个像元高度/μm 4.8 焦距/mm 15.486 9 畸变参数/m−2 −658.29 中心点横坐标/像素 598.431 中心点纵坐标/像素 491.725 图像宽度/像素 1 280 图像高度/像素 1 024
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表 3 中心线提取算法对比
Table 3 Comparison of centerline extraction algorithms
算法 提取精度
级别用时/ms 方向性 断裂拟合 中心线形状
改变灰度重心法 亚像素 21.8 差 断裂小处拟合 小 区域骨架法 亚像素 10.8 好 不拟合 小
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表 4 闭运算和最小二乘法性能对比
Table 4 Comparison of performance between closed operations and least square method
拟合方法 拟合效果 提取精度 提取速度/ms 复杂度 闭运算 差 亚像素 2.171 简单 最小二乘法 好 亚像素 3.082 较复杂
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表 5 标准模型检测结果
Table 5 Standard model detection results
尺寸/cm 宽度/cm 宽度
误差/%高度/cm 高度
误差/%面积/cm2 面积
误差/%5×5 5.029 0.58 4.9585 −0.83 24.937 −0.25 8×8 8.055 0.69 8.0046 0.06 64.476 0.74 10×8 10.050 0.50 7.9909 −0.11 80.318 0.40 10×10 10.060 0.60 10.080 0 0.80 101.360 1.36
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表 6 煤堆体积测量结果
Table 6 Measurement results of coal pile volume
实际
体积/cm3带速为0.25 m/s 带速为0.5 m/s 带速为1 m/s 测量值/cm3 误差/% 测量值/cm3 误差/% 测量值/cm3 误差/% 295.4 301.6 2.09 302.0 2.21 304.9 3.22 498.0 503.7 1.14 508.9 2.19 514.8 3.37 617.4 633.0 2.53 639.7 3.61 640.5 3.74 725.7 745.9 2.78 749.4 3.27 753.9 3.89 773.4 792.5 2.47 794.6 2.73 795.4 2.84 956.5 957.0 0.05 968.5 1.26 973.5 1.78
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