A conveyor belt characterization information extraction method based on YOLOv5 and the skeleton method

Abstract

Abstract To resolve the inaccurate localization of conveyor belt surface damage identification problem and to address the insufficiencies of the methods for extracting surface characterization information, this paper proposes a conveyor belt characterization information extraction method that integrates YOLOv5 deep learning and the skeleton method. By constructing a conveyor belt surface damage recognition model based on the YOLOv5 target detection algorithm, the identification, localization and cropping of the conveyor belt’s surface damage are implemented. After that, edge extraction and surface information extraction are also performed on the damaged parts. Finally, the collected data are analyzed and processed in real time by edge computing equipment to determine the degree of damage of the parts. Finally, intelligent operation of the belt conveyor is achieved with autonomous operations, unattended operations and decision alarms. The experimental results show that the recognition accuracy of YOLOv5 is approximately 93.11%, the speed is approximately 57 frames per second and the error of the data acquired by image processing is between 2% and 10%, which meets the real-time detection requirements of conveyor belt surface damage detection, and assists in the safety management supervision of the belt conveyer.