This work proposes a new method for characterizing pellet feed, employing Deep Learning (DL) and Convolutional Neural Networks (CNNs). The main minerals in the composition of the studied pellet feed are hematite, magnetite, goethite, and quartz. Over time, several characterization methodologies have been developed that use Digital Microscopy and Image Analysis tools. The greatest difficulties in this characterization lie in differentiating the textures of hematite particles, the different shapes of their crystals, or discriminating between quartz and resin in reflected light optical microscopy images. This work proposes a mineral characterization methodology based on the Mask R-CNN algorithm. The goal is to perform instance segmentation, that is, to identify, classify, and segment objects in the images. Two DL models were combined: the BF Model performs instance segmentation for the compact, porous, martite, and goethite classes in images obtained in Bright Field mode, and the CPOL Model uses images acquired in Circularly Polarized Light to segment the monocrystalline, polycrystalline, and martite classes. An F1-score of around 80% was obtained for the BF Model and around 90% for the CPOL Model. The results were promising and can be improved as the training image database increases.

中文翻译：

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使用数字显微镜和深度学习进行颗粒饲料相表征的方法


这项工作提出了一种利用深度学习 (DL) 和卷积神经网络 (CNN) 表征颗粒饲料的新方法。所研究的颗粒饲料成分中的主要矿物质是赤铁矿、磁铁矿、针铁矿和石英。随着时间的推移，已经开发出几种使用数字显微镜和图像分析工具的表征方法。这种表征的最大困难在于区分赤铁矿颗粒的纹理、其晶体的不同形状，或在反射光光学显微镜图像中区分石英和树脂。这项工作提出了一种基于 Mask R-CNN 算法的矿物表征方法。目标是执行实例分割，即识别、分类和分割图像中的对象。组合了两个 DL 模型：BF 模型对在明场模式下获得的图像中的致密、多孔、马铁矿和针铁矿类执行实例分割，而 CPOL 模型使用在圆偏振光下获得的图像来分割单晶、多晶和针铁矿。马蒂特班。 BF 模型获得的 F1 分数约为 80%，CPOL 模型获得的 F1 分数约为 90%。结果很有希望，并且可以随着训练图像数据库的增加而得到改进。