Journal of Electrical Engineering and Automation

Intelligent sorting of ore and rock is a crucial process in the field of mineral processing. Traditional methods suffer from low efficiency and poor accuracy. This paper presents the development of an intelligent recognition and sorting system for ore and rock based on machine vision. By constructing a high-performance image acquisition system and employing deep learning algorithms, the system achieves high-precision recognition of ore and rock. Integrated with automated control technology, the system completes the automatic sorting of ore and rock. Experiments show that the system increases sorting accuracy by more than 30% and improves efficiency by 50%, significantly reducing costs. This provides strong support for the intelligent development of the mineral processing industry.

Machine vision; Ore and rock recognition; Intelligent sorting; Deep learning; Automated control