Quantitative Monitoring Method for Conveyor Belt Deviation Status Based on Attention Guidance

The efficient monitoring of the belt deviation state will help to reduce unnecessary abnormal wear and the risk of belt tear. This paper proposes a coupling characterization method involving the prediction box features of the target detection network and the linear features of the conveyor belt edge to achieve the quantitative monitoring of conveyor belt deviations. The impacts of the type, location, and number of attention mechanisms on the detection effect are fully discussed. Compared with traditional image-processing-based methods, the proposed method is more efficient, eliminating the tedious process of threshold setting and improving the detection efficiency. In detail, the improved practice and tests are carried out based on the Yolov5 network, and the Grad-CAM technique is also used to explore the effect of attention mechanisms in improving the detection accuracy. The experiments show that the detection accuracy of the proposed method can reach 99%, with a detection speed of 67.7 FPS on a self-made dataset. It is also proven to have a good anti-interference ability and can effectively resist the influence of the conveying material flow, lighting conditions, and other factors on the detection accuracy. This research is of great significance in improving the intelligent operation and maintenance level of belt conveyors and ensuring their safe operation.