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Research on Intelligent Identification of Rock Thin Section Minerals Based on Deep Learning
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Lin SUN1, 2, 3, Yan LI1, 3, Xulong YAO2, 3, Zhigang TAO4, Youbang LAI5, Chong CAO6
Mining Research and Development | 2025, 45(10) : 224 - 235
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Mining Research and Development | 2025, 45(10): 224-235
Research on Intelligent Identification of Rock Thin Section Minerals Based on Deep Learning
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Lin SUN1, 2, 3, Yan LI1, 3, Xulong YAO2, 3, Zhigang TAO4, Youbang LAI5, Chong CAO6
Affiliations
  • 1.College of Artificial Intelligence, North China University of Science and Technology, Tangshan, Hebei 063210, China
  • 2.College of Mining Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China
  • 3.Mine Green Intelligent Mining Technology Innovation Center of Hebei Province, Tangshan, Hebei 063210, China
  • 4.State Key Laboratory of Deep Geotechnical Mechanics and Underground Engineering, Beijing 100083, China
  • 5.Sijiaying Yanshan Iron Mine Co., Ltd., Hebei Iron and Steel Group, Tangshan, Hebei 063210, China
  • 6.School of Emergency Management and Safety Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China
Published: 2025-10-25
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With the continuous progress of mineral resources exploration technology, the intelligent identification of rock minerals has become increasingly important in the field of mineral composition analysis. In order to analyze the influence of complex texture structure and variable mineral morphology of rock thin section images on intelligent identification technology, an intelligent identification model of rock minerals based on improved YOLOv8 algorithm (Mineral-YOLO model) was proposed. The Mineral-YOLO model innovatively integrates the LSK module to enhance the identification capacity of the model for different target and background information differences. The ODConv technology is introduced to reduce the influence of background interference, thereby improving the performance of the convolutional network. The loss function is optimized to improve the accuracy mAP of bounding box positioning. In the model training, the self-built data set was extended using the combination enhancement technology, so that the samples of the data set were more abundant. The validation set was used to verify the trained model. The results show that the mean average accuracy of the proposed mineral intelligent identification model is 83.3% and F1 is 78% when identifying 6 kinds of minerals. Compared with the YOLOv8 model, it is increased by 3 percentage points and 1 percentage points respectively, which proves the high efficiency and accuracy of the Mineral-YOLO model in the intelligent identification of rock minerals.

Rock thin section image  /  Intelligent identification  /  Deep learning  /  Mineral-YOLO model  /  Object detection
Lin SUN, Yan LI, Xulong YAO, Zhigang TAO, Youbang LAI, Chong CAO. Research on Intelligent Identification of Rock Thin Section Minerals Based on Deep Learning[J]. Mining Research and Development, 2025 , 45 (10) : 224 -235 .
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Year 2025 volume 45 Issue 10
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Article Info
  • Receive Date：2024-10-31
  • Online Date：2026-02-06
  • Published：2025-10-25
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  • Received：2024-10-31
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Affiliations
    1.College of Artificial Intelligence, North China University of Science and Technology, Tangshan, Hebei 063210, China
    2.College of Mining Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China
    3.Mine Green Intelligent Mining Technology Innovation Center of Hebei Province, Tangshan, Hebei 063210, China
    4.State Key Laboratory of Deep Geotechnical Mechanics and Underground Engineering, Beijing 100083, China
    5.Sijiaying Yanshan Iron Mine Co., Ltd., Hebei Iron and Steel Group, Tangshan, Hebei 063210, China
    6.School of Emergency Management and Safety Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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