Mineral Identification Based on Deep Learning Using Image Luminance Equalization

Abstract

Mineral identification is an important part of geological research. Traditional mineral identification methods heavily rely on the identification ability of the identifier and external instruments, and therefore require expensive labor expenditures and equipment capabilities. Deep learning-based mineral identification brings a new solution to the problem, which not only saves labor costs, but also reduces identification errors. However, the accuracy of existing recognition efforts is often affected by various factors such as Mohs hardness, color, picture scale, and especially light intensity. To reduce the impact of light intensity on recognition accuracy, we propose an efficient deep learning-based mineral recognition method using the luminance equalization algorithm. In this paper, we first propose a new algorithm combining histogram equalization (HE) and the Laplace algorithm, and use this algorithm to process the luminance of the identified samples, and finally use the YOLOv5 model to identify the samples. The experimental results show that our method achieves 95.6% accuracy for the identification of 50 common minerals, achieving a luminance equalization-based deep learning mineral identification method.

Keywords

• deep learning
• image enhancement
• mineral identification
• convolutional neural networks