Intelligent Identification of Minerals Polished Section Under Microscope Based on SSD and Image Transformation

doi:10.12068/j.issn.1005-3026.2025.20230347

Abstract

Abstract:

In mineral identification， there may sometimes be cases of missed identification or incorrect identification when identifying associated minerals. To address these issues， the intelligent mineral identification methods under the microscope were developed. Firstly， an intelligent identification method was constructed by improving the SSD network and incorporating image transformation. Secondly， the method was applied to microscope images of minerals in polished sections from the iron ore in Liaoning Province， China， and its accuracy was demonstrated through tests. Finally， the effects of learning rate and batch size on the loss function were determined， and the accuracy was further improved by using the gradient descent method. In the tests， the identification accuracy exceeds 90%， reaching up to 100%， with the minimum value of the loss function was approximately 0.008. The results indicate that the proposed method has strong mineral identification capabilities.

Key words: mineral identification, deep learning, single shot multibox detector （SSD）, image transformation（IT）, mineral content estimation

CLC Number:

P 575

Zhen-long HOU, Jin-rong SHEN, Ji-kang WEI, Wen-tian ZHAO. Intelligent Identification of Minerals Polished Section Under Microscope Based on SSD and Image Transformation[J]. Journal of Northeastern University(Natural Science), 2025, 46(6): 131-137.

Figures/Tables 9

Fig.1 Network structure of improved SSD

Fig.2 Flow chart of SSD-IT

Fig.3 Schematic diagram of test data enhancement

Fig.4 Data labeling and identification results of SSD-IT image

Table 1 Evaluation results of different identification models

模型	识别精度	召回率	F0.5-score	F1-score	F2-score
SSD-IT	91.19	80.91	88.93	85.74	82.78
SSD	24.46	40.00	26.33	30.36	35.40
Faster-RCNN-IT	40.99	52.62	42.88	46.08	49.79
Faster-RCNN	9.63	17.78	10.60	12.49	15.21
YOLOv5s-IT	40.30	1.45	6.33	2.80	1.80
YOLOv5s	11.80	4.35	8.79	6.36	4.98

Fig.5 Identification precision for the microscopic images from polished sections

Fig.6 Loss values for different learning rates

Fig.7 Loss values for different batch sizes

Fig.8 Comparison of the images before and after the transformation

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