Traditional belt conveyor abnormal state recognition uses manual inspection or mechanical comprehensive protection system for detection. The manual inspection is labor-intensive, inefficient, and ...difficult to accurately detect faults. Mechanical comprehensive protection system is prone to misjudgment and poor recognition effect. The above methods can no longer meet the needs of coal industry intelligence. With the development of machine vision, deep learning, and industrial Ethernet technology, video AI technology has become a research hotspot for intelligent recognition of abnormal states of coal mine belt conveyors. This paper analyzes the current research status of using video AI technology to identify abnormal states of coal mine belt conveyors, such as belt deviation, idler failure, personnel invasion, unsafe behavior of personnel, coal stacking, and foreign objects. It is pointed out that there are three main problems in the current video AI recognition technology for abnormal states of coal mine belt c
Machine vision has a certain theoretical basis in target detection and recognition for sorting robot of coal mine belt conveyor. But current target recognition of sorting robot of coal mine belt ...conveyor is mainly aimed at coal-gangue recognition. There are few kinds of research on the recognition of foreign object targets causing conveyor belt penetration and tearing, and also few kinds of research on the precise positioning of target foreign object. In order to solve the above problems, a foreign object recognition and positioning system based on machine vision for sorting robots of coal mine belt conveyor is designed. The system can recognize and position different types and shapes of foreign objects on the conveyor belt. The image information of the foreign objects on the conveyor belt in real-time is obtained by adopting binocular vision, and the image is preprocessed. Image information is enhanced based on the Canny operator. The gray stretching method is used to improve image edge information to highli
