Research on material flow measurement of belt conveyor based on orthogonal machine vision

The accuracy of traditional visual flow measurement is often compromised due to the variation in the particle state of the material being transported by the belt conveyor. To address this issue, this study proposes a material flow measurement algorithm based on orthogonal machine vision. The algorithm begins by identifying the material type in the RR-CNN image, followed by establishing the material equivalent structure and visual measurement structure. Parameters are then calculated based on the movement state of the material. To improve the filtering effect, K-means clustering filtering is employed, along with a seed region growing algorithm that enhances particle shadow characteristics and image edges. Additionally, volume measurement of different filling densities is achieved using a differential grid and three-dimensional micro-elements, enabling the calculation of the mass of the collected material. Experimental results demonstrate clear particle characteristics and edges, resulting in cost savings for material measurement and a visual measurement accuracy exceeding 95%.