A deep learning approach for accurate assessment of conveyor belt wear state based on multispectral imaging

Accurate assessment of the conveyor belt wear state is a crucial part of measuring belt conveyor safety and reliability. Therefore, this paper proposes an accurate detection approach for conveyor belt wear based on multispectral imaging(MSI), and designs a lightweight network model, named depthwise shuffle coordinate attention network (DSCANet) to assess and classify conveyor belts in three wear states. The multispectral images of the conveyor belt in the Huainan mining area were collected by the MSI system, with a wavelength range of 675–975 nm. The multispectral data at the wavelength with the largest imaging differences was screened as the input to the assessment model DSCANet. Compared with other widely used neural network models, the proposed DSCANet demonstrated the best performance, achieving a classification accuracy of 98.78 %, with floating point operations(FLOPs) of only 136.53M. The findings indicate the great efficacy of the MSI and DSCANet combination in assessing the conveyor belt wear, holding importance in reducing the risk of sudden failures and enhancing production efficiency.