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.

As an important part of modern coal mine production, conveyor belts are widely used in the coal collection and transportation. In order to ensure the safe operation of the coal mine conveyor belt and solve the drawbacks of the existing conveyor belt longitudinal tear detection technology, a multispectral visual detection method for conveyor belt longitudinal tear is proposed in this paper. The experimental results show that the multispectral visual detection method not only can identify the conveyor belt longitudinal tear, but also accurately classifies and identify other states of the conveyor belt. The accuracy of multispectral visual detection method is over 90.06%, and the precision of longitudinal tearing recognition is over 92.04%. The proposed method is verified to meet the requirements of reliability and real-time in the industrial field.