Intelligent mineral image recognition based on improved ConvNeXt network

LI Lei; LU Caiwu; JIANG Song; JING Wengang; WANG Luofeng

doi:10.12097/gbc.2022.08.005

2024 Vol. 43, No. 7

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Article navigation > Geological Bulletin of China > 2024 > 43(7): 1266-1275

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LI Lei, LU Caiwu, JIANG Song, JING Wengang, WANG Luofeng. 2024. Intelligent mineral image recognition based on improved ConvNeXt network. Geological Bulletin of China, 43(7): 1266-1275. doi: 10.12097/gbc.2022.08.005

Citation:

LI Lei, LU Caiwu, JIANG Song, JING Wengang, WANG Luofeng. 2024. Intelligent mineral image recognition based on improved ConvNeXt network. Geological Bulletin of China, 43(7): 1266-1275. doi: 10.12097/gbc.2022.08.005

Intelligent mineral image recognition based on improved ConvNeXt network

1.
College of Resource Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China
2.
Xi’an Key Laboratory for Intelligent Industrial Perception, Calculation and Decision, Xi'an University of Architecture and Technology, Xi’an 710055, Shaanxi, China
3.
Luoyang Luanchuan Molybdenum Industry Group Co., Ltd., Luoyang 471500, Henan, China

More Information

Corresponding author: LU Caiwu, lucaiwu@126.com

Received Date: 08 August 2022

Revised Date: 08 October 2022

Publish Date: 15 July 2024

Abstract

Abstract

Mineral identification is a critical task in geological research, yet accurately identifying minerals remains a significant challenge. This study proposes an intelligent mineral image recognition model based on an improved ConvNeXt network, which utilizes transfer learning strategies and incorporates channel attention mechanisms to address the morphological characteristics of minerals.Firstly, the ConvNeXt network model pre-trained on the ImageNet dataset is employed and integrated into the mineral recognition model through transfer learning. Secondly, based on the ConvNeXt network, the model enhances feature fusion capabilities by combining the ConvNeXt blocks with attention mechanisms. Finally, a dataset comprising 34576 ore images of 26 mineral categories is used, divided into training, validation, and test sets in a 6∶2∶2 ratio. During experimental training, the proposed model demonstrates a significantly faster convergence compared to VGG19, GoogLeNet, ResNet50, ResNeXt50, and the ConvNeXt networks.Experimental results indicate that the intelligent mineral recognition model achieves an accuracy, precision, and recall of 98.58%, 98.62%, and 98.73%, respectively. Ablation experiments confirm that the optimization methods proposed in this study enhance model performance. Additionally, comparative visual analysis of feature maps from different models substantiates that the proposed mineral recognition model accurately extracts mineral features, further validating the model's effectiveness and improving mineral identification accuracy.
- mineral image /
- ConvNeXt /
- transfer learning /
- attention mechanism /
- mineral recognition

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References

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