Design and Application of Visual Inspection System for Running Status of Belt Conveyor

A new conveyor operation status visual detection system is proposed to address the current situation of poor coal flow recognition accuracy and slow response speed in belt conveyor systems, which cannot meet the requirements of coal quantity belt speed adaptive matching control. The system can accurately monitor the coal flow on the conveyor through visual detection and deep learning. The system utilizes a visual monitoring system to extract, segment, enhance and analyze images of coal flow, and utilizes deep learning theory to optimize data analysis and calculation schemes for coal flow detection. According to practical applications, the new control system has a detection deviation of less than 0.2% for coal flow, laying the foundation for achieving automatic adjustment of coal quantity belt speed of the conveyor and improving its operational economy.