Research on high-precision coal flow detection of belt conveyors based on machine vision

In response to the problems of missing image details and poor fitting effect in multiple fractures or areas with large fracture spacing in existing machine vision based coal flow detection methods for belt conveyors, a high-precision coal flow detection system for belt conveyors based on machine vision is proposed. It is based on the principle of direct beam oblique collection laser triangulation. The line laser emitter is arranged directly above the measurement position of the belt conveyor and vertically irradiates the coal pile. The coal pile moves uniformly with the belt conveyor, and a camera at an oblique angle is used to capture real-time images of the surface of the coal pile containing laser stripes. The method calibrates the coal flow detection system, including camera internal parameter calibration and laser plane calibration, to obtain the height information of the coal pile. The processing of laser stripe images on coal flow cross-sections is carried out. The gray center of gravity method and regional skeleton method are compared and analyzed from multiple perspectives such as extraction precision and algorithm real-time performance. Based on the comparison results, the regional skeleton method is selected to extract the center of laser stripes. Aiming at the problem of poor fitting effect of laser stripe fracture repair using image dilation operation, the least squares method is proposed as the laser stripe fracture repair algorithm. Compared with closed operations, the least squares method has better smoothing effect and higher precision in fitting processing. The method establishes a coal flow cross-sectional area calculation model. By calculating the cross-sectional area of the coal pile at each frame, the coal flow volume at different belt speeds can be obtained. The experimental results show that when the belt speeds are 0.25, 0.5, and 1 m/s respectively, the detection system errors are relatively small, with maximum errors of 2.78%, 3.61%, and 3.89%. It verifies that the coal flow detection system has high accuracy.