Working Paper Communication Version 1 This version is not peer-reviewed

Mineral Identification based on Deep Learning that Combines Image and Mohs Hardness

Version 1 : Received: 5 August 2020 / Approved: 6 August 2020 / Online: 6 August 2020 (10:14:18 CEST)

How to cite: Zeng, X.; Ji, X.; Xiao, Y.; Wang, G. Mineral Identification based on Deep Learning that Combines Image and Mohs Hardness. Preprints 2020, 2020080149 Zeng, X.; Ji, X.; Xiao, Y.; Wang, G. Mineral Identification based on Deep Learning that Combines Image and Mohs Hardness. Preprints 2020, 2020080149

Abstract

Mineral identification is an important part of geological analysis. Traditional identification methods rely on either the experiences of the appraisers or the various measuring instruments and the methods are either easily influenced by the experiences or need too much work. To solve the above problems, there have been studies using image recognition and intelligent algorithms to identify minerals. But the current studies can not identify many minerals, and the accuracy is low. To increase the number of identified mineral categories and the accuracy, we proposed the method that uses both the mineral images and the Mohs hardness in the deep neural networks to identify the minerals. The experimental results showed that the method can reach 94.0% Top-1 accuracy and 99.9% Top-5 accuracy for 28 common minerals. The model that combines image and Mohs hardness together can identify more minerals and increase the accuracy using less training data.

Subject Areas

mineral identification; deep learning; convolutional neural network; image; Mohs hardness

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