# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 1 Introduction
It used a system made of a laser generator and camera to gauge the quantity of load from multiple angles by using area proportion and  
Figure 1: Schematic diagram of a conveyor system installed in the soda ash manufacturing industry. laser-based techniques. The particle velocity of the conveyor system is a determinant of issues that may arise if it is not at its optimal level. In case of an increase in velocity, there is more dust generation, more wear, particle attrition, and more noise. On the contrary, a decrease in velocity results in stagnation zones which causes spillage or blockage. Given the context, it is essential to maintain the particle flow through analysis using techniques, such as the discrete element method (DEM) [13], experimental analysis, and continuum-based analytical methods. In this regard, [PERSON] & [PERSON] [14] presented their findings for the methods mentioned above for granular cohesionless techniques. Using high-speed video, they used two continuum-based analytical analyses and captured the conveyor flow. The DEM method was then used to quantify and estimate the particle velocity in a spoon-hood conveyor system. [PERSON] et al. [10] also analyzed the impact of a conveyor belt system's speed regulation and energy consumption with variable speed energy-saving controllers related to material flow. They propose an energy saver conveyor belt model with optimal speed regulation for reducing high energy consumption using a polynomial regression-fitting algorithm which applies regression on univariate polynomials and then uses Back Propagation Neural Network Analysis. [PERSON] & [PERSON] [7] built a multi-camera system that tracks the movement of parcels on a conveyor belt after considering its dimensions using stereo cameras. A controller is fed with the corner points' information, allowing it to arrange the parcels in a line. They use Lucas-Kanade-Tomasi (LKT) algorithms [15; 16] for continuous feature tracking in each frame. The technique is further refined with the addition of edge mapping as a post step. Research in [17] explains techniques for volume and size distribution of load transported on a conveyor system. One technique used for data acquisition was the laser triangulation method, which was adopted to acquire the three-dimensional shape of the rock. A real-time measurement of flow of material on a conveyor belt is proposed in [18]. It used a dual-field measurement system comprised of a couple of light sources to lighten up both the upper and lower surfaces of the belt. On top of that, two binocular cameras are appended to provide a dual field of vision. [PERSON] et al. [19] discusses an approach to estimate run-of-mine or composition on a conveyor line system. They explain that the material on the belt varies in size, grindability, and composition. A solution to measure the speed of the belt using computer vision and machine learning techniques is presented in [20]. Given the importance of this task for efficient and secure operation. The focus here is on a contactless solution instead of a conventional setting, which could provide better accuracy as the measuring instrument will not have to deal with flow issues and would avoid wearing off. They adopted a CCD camera to capture the side of the belt. The speed of the belt was obtained by image texture regularity. [PERSON] et al. [21] worked on the problem of detecting belt deviation as belt conveyor systems often face deflection problems in operation. The researchers thus presented a method to use computer vision to detect belt deviation. They use a combination of the Canny edge detector [22], a belt-positioning algorithm founded on the Hough line detection technique [23; 24], and morphological processing techniques.