Intelligent monitoring method for conveyor belt misalignment based on deep learning

Existing methods for monitoring conveyor belt misalignment face challenges in terms of practicality, robustness, and the difficulty of dataset creation. This paper proposed an intelligent monitoring method for conveyor belt misalignment based on deep learning. First, the conveyor belt edge recognition problem was treated as a line detection issue in a specific scenario. A strategy was proposed to detect the straight lines of the conveyor belt edges using the diagonal features of the bounding box predicted by the object detection network. Specifically, the top-right to bottom-left diagonal of the predicted bounding box was used to represent the left edge of the conveyor belt, and the top-left to bottom-right diagonal was used to represent the right edge. The YOLOv5 model was employed to detect the conveyor belt edges, and a misalignment calculation method and misalignment state determination rules were developed. Experimental results demonstrated that the diagonal features of the predicted bounding box could stably and efficiently achieve conveyor belt edge recognition and misalignment quantification, thereby simplifying image data processing and annotation tasks. The method exhibited strong generalization ability and rapid transfer learning capability. The YOLOv5 model, combined with the line detection strategy, showed excellent anti-interference performance for detecting material flow boundaries, support pillars, and other straight lines. On the CUMT-BELT dataset, the detection accuracy exceeded 99%, with a maximum detection speed of 148 frames per second, ensuring excellent real-time performance.