基于双目视觉深度感知的带式输送机煤量检测方法

Detection method of coal quantity in belt conveyor based on binocular vision depth perception

针对带式输送机存在煤料图像难以识别,以及煤料内部颗粒间隙非均匀随机分布导致体积难以计算的问题,提出了一种由运输煤料识别模块、运输煤料三维信息提取模块和运输煤料量计算模块3部分组成的基于双目视觉深度感知的带式输送机煤量检测方法。其中,运输煤料识别模块采用小波变换算法增强运输煤料图像,并结合K-means聚类算法分割出煤料图像;然后,运输煤料三维信息提取模块采用双目视觉方法获取煤料图像各点的深度信息,从而得到运输煤料三维点云信息;最后,运输煤料量计算模块将Delaunay算法与T-S模糊推理算法相结合,求得煤料体积,进而应用煤量计算公式实现煤量的检测。利用实际图像进行了实验研究,结果表明所提方法的有效性。

In terms of the difficulties in identifying the coal image and calculating coal volume in non-uniform distributed coal particles,this paper proposes a binocular vision detection method for coal quantity in belt conveyor.The method consists of three modules,namely,identification module,three-dimensional information extraction module and quantity calculation module.First,the identification module uses wavelet transform algorithm and K-means clustering algorithm to enhance and segment the coal image,respectively; Then,the three-dimensional information extraction module is employed to obtain the depth information of each point of coal image using binocular vision method,so as to get the three-dimensional point cloud information of coal; Finally,the quantity calculation module combines Delaunay and TS fuzzy reasoning algorithms to calculate the coal volume,and further adopts coal calculation formula to achieve the coal quantity detection.Experimental studies curried out using the actual images show the effectiveness of the proposed method.