Adaptive Detection Method for Belt Conveyor System Material Flow Based on Machine Vision
ZHANG Peng
KANG Wenjian
GONG Guoyi
ZOU Rufeng
CHEN Feng
Abstract：In the process of coal mine transportation with conveyor belts,there are often challenges such as production line stagnation,equipment damage,and material waste caused by material blockage at the feeding port.Traditional manual inspection methods have defects such as delayed response,high missed detection rate,and dependence on manpower,which affect the accuracy of material flow volume detection.A machine vision based adaptive detection method for conveyor belt material flow was proposed based on the above problems.By integrating laser line scanning and high-resolution camera equipment,real-time image data of the discharge port was collected,and an image enhancement algorithm based on wavelet transform was used to optimize the image quality under complex lighting conditions.Combining optical flow analysis technology to dynamically capture material motion trajectories,and utilizing consistency analysis of material flow area and direction to achieve material blockage warning.Finally,a dynamic coal volume measurement model was established.The experimental results showed that this method could accurately detect the volume of coal flow,with an average relative error of 1.613％,which meets the actual operational error requirements.In addition,the constructed artificial intelligence platform can trigger real-time blockage alarms,effectively improving monitoring response speed and accuracy.
Keywords：machine visionbelt conveyor systemplugginglaser lineoptical flow method
Publication Date：2025-08-30
Online Publishing Date：2025-09-23(First online date of this platform, not the publication date of the document)
Pages：7( 47-53 )