摘要
传统的带式输送机煤流检测装置中,核子胶带秤存在一定安全和环保隐患,电子胶带秤检测精度易受输送带张力、刚度等因素的影响;而基于超声波、线激光条纹、双目视觉等技术的非接触式检测方法存在实时性差、测量误差较大等问题。提出了一种基于飞行时间(TOF)深度图像修复的输送带煤流检测方法。通过TOF相机获取输送带运煤图像;对TOF图像进行均衡化处理,采用帧差法和边界跟随算法去除背景噪声,获得感兴趣的煤料区域;针对TOF深度图像因边缘处存在飞行像素噪声与多径误差噪声而导致的边缘信息不准确问题,提出强度图像引导的深度图像修复算法,通过Canny边缘检测算法寻找深度图像和强度图像的相似边缘,基于强度图像的有效边缘信息对深度图像边缘处的不可靠数据进行校正,并进一步基于Navier-Stokes方程和中值滤波器得到高精度深度图像;对煤料区域进行像素级分割,并建立煤料体积计算模型,结合输送带速度得出输送带煤流。实验结果表明,该方法的检测误差不超过3.78%,标准差不超过0.491,平均处理时间为83 ms,满足实际生产要求。
In the traditional belt conveyor coal flow detection device,the nuclear belt scale has certain safety and environmental protection hidden dangers,and the detection precision of electronic belt scale is easily affected by the factors such as belt tension and stiffness.Moreover,non-contact detection methods based on technologies such as ultrasound,linear laser stripes and binocular vision have problems such as poor real-time performance and large measurement errors.A coal flow detection method for conveyor belt based on time-of-flight(TOF)depth image restoration is proposed.The TOF camera is used to obtain the coal conveying image of the conveyor belt.The TOF image is equalized,and the frame difference method and the boundary following algorithm are used to remove the background noise and obtain the coal region of interest.In order to solve the problem of inaccurate edge information caused by flying pixel noise and multi-path error noise at the edge of TOF depth image,the intensity image-guided depth image restoration algorithm is proposed.The Canny edge detection algorithm is used to find similar edges between the depth image and the intensity image.Based on the effective edge information of the intensity image,the unreliable data of the edge of the depth image is corrected.Furthermore,the high-precision depth images are obtained based on Navier-Stokes equation and median filter.The coal area is divided at the pixel level,the coal volume calculation model is established to obtain coal flow of conveyor belt by combining the conveyor belt speed.The experimental results show that the detection error is less than 3.78%,the standard deviation is less than 0.491 and the average processing time is 83 ms,which meets the actual production requirements.
作者
汪心悦
乔铁柱
庞宇松
阎高伟
WANG Xinyue;QIAO Tiezhu;PANG Yusong;YAN Gaowei(Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China;College of Physics and Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China;Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 CD Delft, Netherlands;College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China)
出处
《工矿自动化》
北大核心
2022年第1期40-44,63,共6页
Journal Of Mine Automation
基金
国家自然科学基金面上项目(61973226)
山西省重点研发计划项目(201903D121143)
国家自然科学基金山西省煤基低碳联合基金资助项目(U1810121)
中央指导地方科技发展基金资助项目(YDZX2020140001796)。
