Research on load monitoring technology of mine hoist based on machine vision

Abstract

A hoist load monitoring method based on machine vision technology is proposed in this paper to address the frequent overloading accidents of mine hoists and the low safety and reliability of existing contact load monitoring technologies. The depth image of the skip undergoes time domain and spatial bilateral filtering algorithms for noise reduction, followed by conditional filtering and downsampling algorithms to remove redundant point cloud data. Point cloud recognition, extraction, segmentation, and alignment algorithms are then applied to quickly generate a skip point cloud model. A surface reconstruction optimization process combining greedy projection triangulation algorithm and void repair algorithm is proposed to obtain a smooth and complete sealing of the skip. The closed surface model volume is calculated using VTK volume function. Based on single-rope winding hoist, a load visual monitoring system is constructed for relevant experimental research. Results show that this method can accurately measure the loaded coal volume with a relative error range of 0.05%–4.13%, meeting practical application requirements while providing an effective way for non-contact accurate measurement of hoist loads in mines.