# Mineral Identification Based on Deep Learning Using Image Luminance Equalization
## 1 Introduction
Yolov5 has been used in many practical applications such as face recognition [24] and aircraft target detection [25]. In this paper, we combine image enhancement techniques with YOLOv5 for mineral detection to address the effects of illumination factors on image chromatic aberrations. With this method, we achieved the accurate identification of mineral images without relying on specialized instruments for obtaining identification data. In addition, our method enables the more accurate identification of samples with poor lighting conditions (too bright or too dark) than other efforts to identify minerals based on image data. Moreover, our work expands the range of mineral species that can be identified to a greater extent than other works. Our detailed contributions are shown below. * We first propose a novel image enhancement algorithm, one which combines histogram equalization (HE) and the Laplace algorithm. In subsequent experiments, the algorithm shows powerful results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Studies** & **Characteristics** \\ \hline \multirow{2}{*}{Instrument Observation} & [1] & Wide range of applications. \\  & [2] & Spectrometer with very high pixels. \\ \hline \multirow{2}{*}{Chemical Composition Analysis} & [6] & Fast data acquisition. \\  & [7] & High accuracy of chemical element identification. \\  & [8] & Low sample loss. \\ \hline \multirow{2}{*}{Spectral Analysis} & [9] & Reliable and has international datasets. \\ \hline \multirow{4}{*}{Micro-optical Picture Analysis} & [10] & High accuracy rate. \\  & [11] & Effectively differentiate between quartz and resin. \\ \cline{1-1}  & [12] & Effective mineral grain identification. \\ \cline{1-1}  & [13] & Good results for rock minerals. \\ \cline{1-1}  & [14] & High accuracy of sulfide mineral identification. \\ \cline{1-1}  & [15] & Good performance in petrographic thin sections. \\ \hline \multirow{3}{*}{Traditional Image Analysis} & [16] & Combined with mineral hardness. \\ \cline{1-1}  & [17] & High accuracy of malachite and blue copper mineral identification. \\ \cline{1-1}  & [18] & Be able to distinguish the formation minerals of different granite types. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different mineral identification methods. * We achieved the an efficient identification of 50 minerals, which is a significant expansion of the number of mineral species identified compared to the existing works. * Experiments show that our method achieves 95.6% accuracy in mineral identification, surpassing existing mineral identification methods. The content of this paper is shown as follows. We introduce a novel image enhancement approach in Section 2, combining histogram equalization (HE) and the Laplace algorithm. In Section 3, we focus on the structure of the model we use and briefly describe the training environment and process. In Section 4, we show the results of our experiments and compare them with other methods, in addition to evaluating the model effectiveness using objective evaluation metrics. In Section 5, we conclude the article and propose future work.