基于机器视觉的带式输送机高精度煤流检测研究

季现亮; 张文杰; 王玉强; 刘勇; 田祖织; 付拯

doi:10.13272/j.issn.1671-251x.2024030028

基于机器视觉的带式输送机高精度煤流检测研究

doi: 10.13272/j.issn.1671-251x.2024030028

1.
临沂矿业集团菏泽煤电有限公司郭屯煤矿，山东菏泽　274000
2.
中国矿业大学机电工程学院，江苏徐州　221116
3.
山东能源集团鲁西矿业有限公司，山东菏泽　274000

基金项目: 国家自然科学基金资助项目（52375069）。

详细信息

作者简介:
季现亮（1972—），男，山东齐河人，工程师，研究方向为智能矿山技术，E-mail：997476358@qq.com

通讯作者:
张文杰（1998—），男，江苏徐州人，硕士研究生，研究方向为智能矿山技术，E-mail: 714849265@qq.com。

中图分类号: TD634
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- 被引次数: 0
出版历程
- 收稿日期: 2024-03-12
- 修回日期: 2024-05-26
- 网络出版日期: 2024-06-13

Research on high-precision coal flow detection of belt conveyors based on machine vision

1.
Guotun Coal Mine, Linyi Mining Group Heze Coal and Electricity Co., Ltd., Heze 274000, China
2.
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
3.
Shandong Energy Group Luxi Mining Co., Ltd., Heze 274000, China

摘要

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。
- 带式输送机 /
- 煤流检测 /
- 机器视觉 /
- 线激光发射 /
- 激光三角测量 /
- 激光条纹中心提取 /
- 激光条纹断裂修补 /
- 区域骨架法 /
- 最小二乘法
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.
- belt conveyor /
- coal flow detection /
- machine vision /
- linear laser emission /
- direct beam oblique collection laser triangulation /
- laser stripe center extraction /
- laser stripe fracture repair /
- regional skeleton method /
- least squares method

HTML全文

图 1 直射斜收式激光三角测量原理

Figure 1. Principle of direct oblique receiving laser triangulation

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图 2 煤流检测系统硬件平台

Figure 2. Coal flow detection system hardware platform

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图 3 标定相机内参时采集的图像

Figure 3. Images captured during camera calibration

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图 4 畸变校正前后对比

Figure 4. Comparison before and after distortion correction

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图 5 低位置图像

Figure 5. Low-position images

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图 6 高位置图像

Figure 6. High-position images

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图 7 红色通道分离前后激光条纹

Figure 7. Laser stripes before and after red channel separation

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图 8 图像滤波前后激光条纹

Figure 8. Laser stripes before and after image filtering

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图 9 阈值分割激光条纹

Figure 9. Threshold segmentation of laser stripe

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图 10 激光条纹灰度分布

Figure 10. Laser stripe gray distribution

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图 11 激光条纹中心线提取结果

Figure 11. Laser stripe centerline extraction results

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图 12 形态学断裂修补激光曲线

Figure 12. Morphological fracture repair laser curve

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图 13 最小二乘法激光曲线拟合

Figure 13. Least square method laser curve fitting

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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 15 煤流截面积计算模型

Figure 15. Calculation model of coal flow cross-section

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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

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图 17 煤流体积检测模型

Figure 17. Coal flow volume detection model

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图 18 标准模型

Figure 18. Standard model

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图 19 标准模型激光条纹拟合曲线

Figure 19. Standard model laser stripe fitting curves

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图 20 标准模型世界坐标

Figure 20. Standard model world coordinates

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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
实际体积/cm³	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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