# Mineral Identification Based on Deep Learning Using Image Luminance Equalization
## 1 Introduction  
Mineral identification occupies an important position in geological research. Traditional geological mineral identification methods mainly identify minerals by the naked eye or observation instruments. Naked-eye identification heavily relies on the discriminatory ability of the identifier. Observations through instruments, such as the identification of clay minerals and hydrocarbons by using near-infrared spectroscopy [1], and mineral identification and mineral mapping by imaging spectroscopy [2], require special identification instruments. Both methods are labor intensive and their accuracy is often influenced by the experience and ability level of the identifier. In recent years, researchers have used deep learning techniques to reduce these effects, for example, [PERSON] et al. [3] used artificial neural networks in mineral potential mapping, and [PERSON] et al. [4] used convolutional neural networks based on geological big data for mineral prospect prediction. In mineral identification, many works also use intelligent algorithms, and these methods can be classified into three categories according to the test method and the type of data obtained: identification based on chemical composition analysis; identification based on spectral analysis; and identification based on optical pictures. The main types of data involved in identification methods based on chemical composition analysis [5] are energy scattering spectroscopy (EDS) [6], electron probe (EPMA) [7], and laser-induced breakdown spectroscopy (LIBS) [8]. The identification method [9] based on spectral analysis is the most reliable method for mineral identification, but it requires expensive testing instruments and is therefore difficult to be widely promoted. The optical picture-based identification method is the most common identification method, which can be performed by microscopicimages [10; 11; 12; 13; 14; 15] and ordinary photographs [16; 17; 18]. As shown in Table 1, we summarized the different current mineral identification methods. All of the above studies enable the identification of minerals, but usually only for a small number of species of minerals, and also lack stable and excellent identification accuracy. In addition, one difficulty in using photo-based mineral identification is that mineral photos in the field are often affected by light intensity as well as shadows, resulting in photos with different photometric details, which can easily lead to errors in identification. For example, the same mineral may be pictured in two colors with strong and weak light intensity, and color is one of the important features used for mineral identification. Therefore, it is difficult to achieve high accuracy with a direct identification of photos taken with cell phones or cameras. Studies using image enhancement techniques to eliminate the effects of extraneous factors on photographs have emerged and demonstrated utility in many other applications. For example, [PERSON] et al. [19] investigated a new method to improve the change detection accuracy of synthetic aperture radar (SAR) remote sensing images by combining image enhancement algorithms based on wavelet and spatial domains and power law. In addition, regarding the effect of luminance, [PERSON] et al. [20] relied on Retinex theory and used a two-step approach combining candidate regions and object locations to achieve object recognition in low luminance situations. [PERSON] et al. [21] achieved the identification of ripe litchi under different lighting conditions based on Retinex image enhancement and improved the accuracy of image identification. In more detail, we compare the accuracy of mineral identification approaches based on image type later on, as shown in Section 4.3. There are many models for object detection, such as EfficientDet [22] and YOLOv5. YOLOv5 (as shown in Section 3) extends from YOLOv4 [23], which is one of the most effective object detection models available.