Instance segmentation of quartz in iron ore optical microscopy images by deep learning.

• Iron ore composition is vital for steelmaking and improving mineral processing. • Accurate segmentation of quartz particles is difficult. • Deep Learning methods were used to segment quartz particles. • Mask R-CNN was trained with 865 images containing quartz particles. • The F1-Score was over 90%. Iron ore characterization is essential for the mineral industry since it provides relevant information, such as ores' chemical composition and the textural and morphological aspects of particles, required for designating a proper mineral processing route. Reflected Light Optical Microscopy (RLOM) is typically used in this field, as it allows the identification of most mineral phases by their different reflectances in a fast and low-cost procedure. Among those phases, quartz, a gangue mineral, presents a big challenge due to its transparency, as it displays a similar hue to the resin used for sample mounting. Thus, even though specialists can visually identify quartz particles, their recognition by automatic image processing has remained elusive. In the present work, a Deep Learning Instance Segmentation model was trained to identify and segment quartz particles in iron ore optical microscopy images, employing the state-of-the-art Mask R-CNN architecture. The model was trained with 865 images containing 1402 quartz particles, and 216 images, containing 338 quartz particles, were used to evaluate the performance. The performance metrics reached a precision of 95.22%, a recall of 88.46%, and an F1-Score of 91.72%. [ABSTRACT FROM AUTHOR]