Coarse-Grained Ore Distribution on Conveyor Belts With TRCU Neural Networks

The particle size distribution of ore is a key evaluation indicator of the degree of ore fragmentation and plays a key role in the separation of mineral processing. Traditional ore size detection is often done by manual sieving, which takes a great deal of time and labor. In this work, a deep learning network model (referred to as TRCU), combining Transformer with residual blocks and CBAM attention mechanism in an encoder-decoder structure was developed for particle size detection of medium and large particles in a wide range of particle sizes in an ore material transportation scenario. This model presents a unique approach to improve the accuracy of identifying ore regions in images, utilizing three key features. Firstly, the model utilizes the CBAM attention mechanism to increase the weighting of ore regions in the feature fusion channel; secondly, a Transformer module is used to enhance the correlation of features in coarse-grained ore image regions in the deepest encoding and decoding stages; finally, the residual module is used to enhance useful feature information and reduce noise. The validation experiments are conducted on a transport belt dataset with large variation in particle size and low contrast. The results show that the proposed model can capture the edges of different particle sizes and achieve accurate segmentation of large particle size ore images. The MIoU values of 82.44%, MPA of 90.21%, and accuracy of 94.91% are higher than those of other existing methods. This work proposes a reliable method for automated detection of mineral particle size and will promote the automation level of ore processing.