E3S Web Conf.
Volume 200, 2020The 1st Geosciences and Environmental Sciences Symposium (ICST 2020)
|Number of page(s)||6|
|Section||Land, Water, and Natural Resources|
|Published online||23 October 2020|
Mask R-CNN for rock-forming minerals identification on petrography, case study at Monterado, West Kalimantan
Department of Geological Engineering, Universitas Gadjah Mada, Indonesia
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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