Binocular vision measurement method for coal flow of belt conveyor based on deep transfer learning

Abstract

The invention discloses a binocular vision measurement method for coal flow of a belt conveyor based on deep transfer learning, and performs coal material image preprocessing according to a Bouguet image correction algorithm, a histogram equalization image enhancement algorithm, and a Hough transform image segmentation algorithm. ;Based on the coal material stereo matching data set, the pre-trained PSM-Net model was transferred to learn, and the deep learning model for the coal material stereo matching task was established, and the binocular vision measurement principle was used to calculate the three-dimensional information of the coal material; the triangular prism network was used The volume of the loaded tape is calculated by lattice differential ergodic summation, and the coal flow rate is obtained through the differential calculation of the empty tape and the loaded tape. The invention adopts binocular vision to collect data to realize non-contact measurement, and realizes stable, accurate and fast coal flow carried by belt conveyor through deep transfer learning stereo matching model PSM‑Net, GPU accelerated calculation and differential calculation Calculation, while improving the convenience and practicality of the method.

Description

technical field [0001] The invention relates to an intelligent visual measurement method for coal flow of a belt conveyor, in particular to a binocular visual measurement method for coal flow of a belt conveyor based on deep transfer learning. Background technique [0002] Belt conveyor is one of the important equipment in coal mine production. It is a kind of equipment for transportation by means of friction. It has the characteristics of strong transportation capacity, long transportation distance and continuous transportation. [0003] The coal in the underground fully mechanized mining face is transported to the ground through the trough belt conveyor, the main belt conveyor, the main shaft belt conveyor and the upper warehouse belt conveyor, and the length of the mining belt conveyor can be from Hundreds of meters to nearly a thousand meters. As an important energy-consuming equipment in coal mining enterprises, mining belt conveyors can account for 30% of the total lo...

AI Technical Summary

Problems solved by technology

However, when using binocular cameras for measurement, the influence of complex and changeable underground lighting in coal mining enterprises, dark coal images, and external interference factors are ignored; at the same time, the traditional image stereo matching algorithm has poor adaptability in coal stereo matching tasks.

The color and texture of the coal material image are repetitive and single, and the traditional image stereo matching algorithm needs to perform sliding window matching based on the image pixel information, resulting in the failure of the coal material image stereo matching, which in turn causes the calculation error of binocular vision to measure the coal flow; and the need to carry the coal material Image segmentation with tape, this process is computationally complex and affects system operating efficiency

Images