Image Mining for Intelligent Autonomous Coal Mining

Automation in underground mining enhances safety and leads to economic efficiencies. After more than 50 years of research, automation tasks have gradually been growing out of the classic engineering/mechanical environment and require a sophisticated datamining treatment. The success of (mechanical) process automation has left only one remaining place in the coal-excavation process where human labor is still indispensable: the operation of the excavation machine, the shearer loader. The necessity to utilize human labor is due to the unknown exact position of the underground coal seam to be excavated. Therefore, at each point in time, a human has to detect the direction in which to excavate further. In the harsh underground conditions, this brings about serious safety and health hazards. The operator is surrounded by dust, which obstructs his vision, among others. In addition, human mistakes in coal-layer detection mean that more rock is excavated rather than coal, which lowers the economic efficiency of the whole coal-mining process. In this paper we fill in the gap in the coal-excavation automation by building a pattern recognition system inside the shearer loader that turns it into a “smart” machine that is capable of detecting the correct position of the coal to be excavated and enable human-free coal mining. The success of such a system would mean that mines with very harsh human conditions could potentially become fully operational, since human presence would not any more be required for coal excavation.