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%.

中文翻译：

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通过深度学习对铁矿石光学显微镜图像中的石英进行实例分割

铁矿石表征对于采矿业至关重要，因为它提供了指定适当的选矿路线所需的相关信息，例如矿石的化学成分以及颗粒的结构和形态方面。反射光光学显微镜 (RLOM) 通常用于该领域，因为它可以快速、低成本地通过不同的反射率来识别大多数矿物相。在这些相中，石英（一种脉石矿物）由于其透明度而提出了巨大的挑战，因为它显示出与用于样品安装的树脂相似的色调。因此，尽管专家可以目视识别石英颗粒，但通过自动图像处理对其进行识别仍然难以捉摸。在目前的工作中，使用最先进的 Mask R-CNN 架构训练深度学习实例分割模型来识别和分割铁矿石光学显微镜图像中的石英颗粒。该模型使用包含 1402 个石英颗粒的 865 个图像进行训练，并使用包含 338 个石英颗粒的 216 个图像来评估性能。性能指标达到了 95.22% 的准确率、88.46% 的召回率和 91.72% 的 F1-Score。