|
|
|
| Volume Measurement of Coal Volume on Belt Conveyor Based on Image Processing |
| HE Jie,WANG Gui-mei, LIU Jie-hui,YANG Li-jie |
| College of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan, Hebei 056038, China |
|
|
|
|
Abstract In order to realize the real-time measurement of coal volume on belt conveyor, a method of measuring coal volume on belt based on image processing technology is proposed. The coal image of belt conveyor is collected by online laser instrument and industrial camera. First, the image is preprocessed, including filtering, graying and binarization, and then the bone is reduced by image expansion. According to the baseline of no-load coal-free time and the laser line of real-time acquisition, the coal profile is formed by comparison, and the cross-sectional area of coal quantity on belt is calculated. According to the relationship between cross-sectional area and belt speed of belt conveyor, the volume of coal quantity on belt is measured. The maximum error is 4.2%, the minimum error is 0.9%, and the operation is stable.
|
|
Received: 24 June 2019
Published: 08 December 2020
|
|
|
|
|
|
［1］葛世荣, 刘洪涛, 刘金龙, 等. 我国煤矿生产能耗现状分析及节能思路［J］. 中国矿业大学学报, 2018, 47(1):9-14.
Ge S R, Liu H T, Liu J L, et al. Analysis of current situation of energy consumption in coal mine production in China and energy saving ideas［J］. Journal of China University of Mining & Technology,2018,47(1):9-14.
［2］张延芝. 基于激光扫描技术的带式输送机非接触式输送量检测系统的应用与研究［D］. 沈阳:东北大学, 2014.
［3］李阳. 矿用核子皮带秤在线监测系统［D］. 西安:西安科技大学,2017.
［4］雷晓峰, 李烨. 数字工业相机中CMOS传感器的最新发展［J］. 传感器世界, 2017, 23(10):7-11.
Lei X F, Li Y. The Latest Development of CMOS Sensors in Digital Industrial Cameras ［J］. Sensor World, 2017, 23(10): 7-11.
［5］李俊峰. 基于RGB色彩空间自然场景统计的无参考图像质量评价［J］. 自动化学报, 2015, 41(9):1601-1615.
Li J F. No-reference image quality evaluation based on RGB color space natural scene statistics［J］. Acta Automatica Sinica, 2015,41(9):1601-1615.
［6］Mo S Q, Liu Z G, Zhang J, et al. An unsupervised threshold selection method for edge detection based on image self-information［J］. Journal of Optoelectronics.Laser, 2011, 22(8):1246-1250.
［7］张引. 基于空间分布的最大类间方差牌照图像二值化算法［J］. 浙江大学学报(工学版), 2001,35(3):272-275.
Zhang Y.Image Binarization Algorithm for Maximum Inter-class Variance License Plate Based on Spatial Distribution［J］.Journal of Zhejiang University (Engineering Science),2001,35(3):272-275.
［8］易三莉, 张桂芳, 贺建峰, 等. 基于最大类间方差的最大熵图像分割［J］. 计算机工程与科学, 2018, 40(10):1874-1881.
Yi S L, Zhang G F, He J F, et al. Maximum Entropy Image Segmentation Based on Maximum Inter-Class Variance［J］. Computer Engineering and Science,2018,40(10):1874-1881.
［9］龚树锋. 基于机器视觉的道路检测与车辆检测算法研究［D］. 广州:华南理工大学, 2010.
［10］牟少敏, 杜海洋, 苏平, 等. 一种改进的快速并行细化算法［J］. 微电子学与计算机, 2013, 30(1):53-55+60.
Mou S M, Tu H Y, Su P, et al. An Improved Fast Parallel Thinning Algorithm［J］. Microelectronics & Computer, 2013,30(1):53-55+60.
［11］Zhang T Y , Suen C Y. A Fast Parallel Algorithm for Thinning Digital Patterns. Communications of the ACM 27(3)［J］. Communications of the Acm, 1984, 27(3):236-239.
［12］李萍, 任安祥. 基于机器视觉的带送煤炭体积测量方法研究［J］. 工矿自动化, 2018, 44(4):24-29.
Li P, Ren A X. Research on Measurement Method of Coal Volume with Coal Vision Based on Machine Vision［J］.Industry and Automation, 2018,44(4):24-29. |
|
|
|
2020, Vol. 41 
