摘要：This paper explores the experiment of Deep Learning method using Mask Region-Convolutional Neural Network (Mask R-CNN) to identify rock-forming minerals on thin section images from petrographic observation in igneous rocks, which are plagioclase, quartz, K-feldspar, pyroxene, and hornblende. Train and validation dataset consisted of 2 quartz diorites and 1 granodiorite from Monterado, West Kalimantan, 1 quartz diorite and 1 granite from Nangapinoh, West Kalimantan, and 7 andesite and 2 basalts from Bangli, Bali, while test dataset consisted of 3 quartz diorites from Monterado, West Kalimantan. This study uses 4 Mask R-CNN models, which is influenced by the lighting on polarizing microscope and using ResNet-50 architecture (Model A) or ResNet-101 (Model B), and the models that is not affected by the lighting on polarizing microscope and using ResNet-50 architecture (Model C) or ResNet-101 (Model D). From Average Precision scores, it was found that Model B has the highest score (58.0%), followed by Model A (57.8%), Model C (45.8%), and Model D (43.6%). In conclusion, the lighting of polarizing microscope is a major factor to give a better performances of Mask R-CNN models by 12%-14.4%, while the type of backbone architecture on Mask R-CNN models was not too consequential.
