Accurate tracking of underground loader activities and material movements from the face can be imprecise due to the difficulties inherent in underground mining. These factors can include lack of communications network, difficulty tracking equipment position and the need to manually record data. As a result, loader operators often record or report the incorrect work performed leading to erroneous data in material flow. Another commonly observed scenario is that operators call in material movements in bunches rather than individually which hinders the accurate capture of metrics and makes it difficult to analyse performance and optimize production.
CITATION: Higgins, C, 2019. Automating load-haul-dump cycle data capture with machine vision and deep neural networks, in Proceedings Future Mining 2019, pp 1113 (The Australasian Institute of Mining and Metallurgy: Melbourne).