A self-training method uses cross-scan particle matching instead of human labels to train and evaluate 3D particle instance segmentation, reporting 97 percent volume coverage and 54,000+ particles on quartz fragments.
Techniques in high-speed imaging and X-ray micro-computed tomography for characterisation of iron ore fragmentation
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abstract
Fragmentation and breakage of rocks is essential to iron ore mining and extraction. The breakage energy requirements and resulting ore particle size and mineral distributions are key to understanding and optimising mining and processing practices. This study combines high-speed and experimental X-ray micro-CT (micro-computed tomography) imaging with 3D image analyses to study the fragmentation of iron ore particles. Here a case study of a hematite-rich iron ore particle is used to illustrate the application and results produced by this imaging procedure. The particle was micro-CT scanned before a high-speed camera was used to image particle breakage in a custom drop weight test, capturing the dynamic processes of particle fracturing and subsequent fragmentation at a resolution of 50 microseconds. Fragments produced were collected, micro-CT scanned and analysed in three-dimension by particle shape, size, and volume.
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Self-Validated Learning for Particle Separation: A Correctness-Based Self-Training Framework Without Human Labels
A self-training method uses cross-scan particle matching instead of human labels to train and evaluate 3D particle instance segmentation, reporting 97 percent volume coverage and 54,000+ particles on quartz fragments.