Abstract:
Knowing the characteristics of the feed ore size is an important consideration for operations and control of a run-of-mine ore milling circuit. Large feed ore variations are important to detect as they require intervention, whether it be manual by the operator or by an automatic controller. A deep convolutional neural network is used in this work to classify the feed ore images into one of four classes. A VGG16 architecture is used and the classifier is trained making use of transfer learning.
