This article presents an innovative visual method for measuring material mass online by quantified conveyor belt deformation with deep learning, which offers a noncontact and safe alternative to traditional pressure- and radioactivity-based weighing techniques. The correlation between the belt deformation and the carried material mass is further investigated through finite element simulations. Then, a visual weighing method by belt deformation is proposed, comprising a calibration algorithm to construct a measurement model using a gated recurrent unit-based network, and an online measurement algorithm to calculate material mass with the trained network. Finally, a case study is presented to analyze the effect of different dimension configurations and networks. The results validate that the proposed method attains a notable accuracy and is suitable for high-velocity conveyor environments. The demonstrated benefits signify an advancement in visual perception of materials, enabling a new approach for intelligent operation and monitoring in material handling field.

Digital twins and visual monitoring of conveyor systems require accurate digital models of dynamic bulk material flows, but existing methods struggle to achieve both speed and precision. This study develops a rapid online method to reconstruct dynamic bulk material flows on conveyor belts. First, a standardized online reconstruction scheme using visual detection of material flow contour lines is presented. Then, a feature detection algorithm is proposed to extract more refined points from laser line skeleton to accelerate the reconstruction process. An iterative-filtering interpolation algorithm that generates smooth interframe point clouds is introduced to improve mesh quality. Experimental results demonstrate that our method outperforms traditional corner detection-based reconstruction techniques in feature point detection, accuracy, mesh quality, and runtime performance. This research provides a practical solution for material handling digitalization, promoting the advancement of conveyor system digital twins and potentially improving operational efficiency and predictive maintenance in bulk material handling industries.