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Application of Deep Learning and Computer Vision in Petrographic Images...
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Application of Deep Learning and Computer Vision in Petrographic Images Analysis

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Processing and Extractive Metallurgy".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 4906

Special Issue Editor

Dr. Sebastian Iwaszenko

E-Mail Website
Guest Editor
GIG National Research Institute, Plac Gwarków 1, 140-166 Katowice, Poland
Interests: computer science; computer vision; image processing; image analysis; machine learning; artificial intelligence; software engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence and computer vision are becoming indispensable components of our everyday life. This is also particularly true in the case of scientific research, where the application of their achievements not only supports work automation but also creates opportunities for discoveries not possible before.

One such scientific field where computer vision and in particular deep learning is being more and more present is in the analysis of petrographic images. Mineral identification, segmentation, and autonomous interpretation of the thin section petrographic images are only a few examples of many potentials (and nowadays ongoing) applications. Therefore, in this Special Issue, we aim to include original and recent work or reviews in the form of methodologies, technologies, or applications of computer vision and that demonstrate a particular focus on deep learning in petrography. The wide and important area of image analysis via the use of artificial intelligence methods is an exciting field of research. We believe that this Special Issue will be an excellent place to share the research results. We welcome manuscripts relating, but not limited to, the following areas: artificial intelligence, computer vision, deep learning, object detection, image segmentation, petrographic images analysis, maceral images analysis, and microscopic images of mineral matter analysis.

Dr. Sebastian Iwaszenko
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • computer vision
  • deep learning
  • object detection
  • image segmentation
  • petrographic images analysis
  • maceral images analysis
  • microscopic images of mineral matter analysis

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Published Papers (4 papers)

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Research

20 pages, 10364 KiB  
Article
Res-UNet Ensemble Learning for Semantic Segmentation of Mineral Optical Microscopy Images
by Chong Jiang, Alfian Abdul Halin, Baohua Yang, Lili Nurliyana Abdullah, Noridayu Manshor and Thinagaran Perumal
Minerals 2024, 14(12), 1281; https://doi.org/10.3390/min14121281 - 17 Dec 2024
Viewed by 266
Abstract
In geology and mineralogy, optical microscopic images have become a primary research focus for intelligent mineral recognition due to their low equipment cost, ease of use, and distinct mineral characteristics in imaging. However, due to their close reflectivity or transparency, some minerals are [...] Read more.
In geology and mineralogy, optical microscopic images have become a primary research focus for intelligent mineral recognition due to their low equipment cost, ease of use, and distinct mineral characteristics in imaging. However, due to their close reflectivity or transparency, some minerals are not easily distinguished from other minerals or background. Secondly, the number of background pixels often vastly exceeds the number of pixels for individual mineral particles, and the number of pixels of different mineral particles in the image also varies significantly. These have led to the issue of data imbalance. This imbalance results in lower recognition accuracy for categories with fewer samples. To address these issues, a flexible ensemble learning for semantic segmentation based on multiple optimized Res-UNet models is proposed, introducing dice loss and focal loss functions and incorporating a pre-positioned spatial transformer networks block. Twelve optimized Res-UNet models were used to construct multiple Res-UNet ensemble learnings using heterogeneous ensemble strategies. The results demonstrate that the system integrated with five learners using the weighted voting fusion method (RUEL-5-WV) achieved the best performance with a mean Intersection over Union (mIOU) of 91.65 across all nine categories and an IOU of 84.33 for the transparent mineral (gangue). The results indicate that this ensemble learning scheme outperforms individual optimized Res-UNet models. Compared to the classical Deeplabv3 and PSPNet, this scheme also exhibits significant advantages. Full article
(This article belongs to the Special Issue Application of Deep Learning and Computer Vision in Petrographic Images Analysis)
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27 pages, 5400 KiB  
Article
Lithology Identification Based on Improved Faster R-CNN
by Peng Fu and Jiyang Wang
Minerals 2024, 14(9), 954; https://doi.org/10.3390/min14090954 - 21 Sep 2024
Viewed by 945
Abstract
In the mining industry, lithological identification is crucial for ensuring the safety of equipment and personnel, as well as for improving production efficiency. Traditional ore identification methods, such as visual inspection, physical testing, and chemical analysis, have many limitations in terms of their [...] Read more.
In the mining industry, lithological identification is crucial for ensuring the safety of equipment and personnel, as well as for improving production efficiency. Traditional ore identification methods, such as visual inspection, physical testing, and chemical analysis, have many limitations in terms of their operational complexity and applicability. Modern ore identification technologies, especially those combined with deep learning methods, can effectively overcome these shortcomings and significantly enhance identification performance. However, mainstream deep learning object detection algorithms still face the issues of low accuracy and poor identification performance in challenging mining conditions. To handle these problems, an improved Faster R-CNN model is proposed in this study. Specifically, we replace the backbone network ResNet with Res2Net-50 and incorporate an improved Feature Pyramid Network (FPN) to enhance feature fusion, thereby further improving the model’s feature extraction capability. Region of Interest(ROI) Align replaces the ROI pooling layer to solve the spatial misalignment issue, providing a higher detection accuracy in tasks involving small object detection and precise boundary detection. Additionally, the backbone feature extraction network integrates an efficient channel attention (ECA) module to optimize high-resolution semantic information maps. By adding simulated noise, the model’s robustness and anti-interference capabilities are enhanced. Soft-NMS is used instead of traditional NMS, preserving more potential targets through a confidence decay mechanism, thereby improving the detection accuracy and robustness. The experimental results show that the improved Faster R-CNN model maintains efficient and accurate ore identification capabilities even in complex mining environments, demonstrating its great potential in practical applications. The model achieves significant improvements in detection accuracy and efficiency, providing strong support for the intelligent and automated identification of ores. Full article
(This article belongs to the Special Issue Application of Deep Learning and Computer Vision in Petrographic Images Analysis)
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22 pages, 12904 KiB  
Article
Intelligent Classification and Segmentation of Sandstone Thin Section Image Using a Semi-Supervised Framework and GL-SLIC
by Yubo Han and Ye Liu
Minerals 2024, 14(8), 799; https://doi.org/10.3390/min14080799 - 5 Aug 2024
Viewed by 944
Abstract
This study presents the development and validation of a robust semi-supervised learning framework specifically designed for the automated segmentation and classification of sandstone thin section images from the Yanchang Formation in the Ordos Basin. Traditional geological image analysis methods encounter significant challenges due [...] Read more.
This study presents the development and validation of a robust semi-supervised learning framework specifically designed for the automated segmentation and classification of sandstone thin section images from the Yanchang Formation in the Ordos Basin. Traditional geological image analysis methods encounter significant challenges due to the labor-intensive and error-prone nature of manual labeling, compounded by the diversity and complexity of rock thin sections. Our approach addresses these challenges by integrating the GL-SLIC algorithm, which combines Gabor filters and Local Binary Patterns for effective superpixel segmentation, laying the groundwork for advanced component identification. The primary innovation of this research is the semi-supervised learning model that utilizes a limited set of manually labeled samples to generate high-confidence pseudo labels, thereby significantly expanding the training dataset. This methodology effectively tackles the critical challenge of insufficient labeled data in geological image analysis, enhancing the model’s generalization capability from minimal initial input. Our framework improves segmentation accuracy by closely aligning superpixels with the intricate boundaries of mineral grains and pores. Additionally, it achieves substantial improvements in classification accuracy across various rock types, reaching up to 96.3% in testing scenarios. This semi-supervised approach represents a significant advancement in computational geology, providing a scalable and efficient solution for detailed petrographic analysis. It not only enhances the accuracy and efficiency of geological interpretations but also supports broader hydrocarbon exploration efforts. Full article
(This article belongs to the Special Issue Application of Deep Learning and Computer Vision in Petrographic Images Analysis)
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20 pages, 11448 KiB  
Article
Deep Learning for Refined Lithology Identification of Sandstone Microscopic Images
by Chengrui Wang, Pengjiang Li, Qingqing Long, Haotian Chen, Pengfei Wang, Zhen Meng, Xuezhi Wang and Yuanchun Zhou
Minerals 2024, 14(3), 275; https://doi.org/10.3390/min14030275 - 5 Mar 2024
Viewed by 1857
Abstract
Refined lithology identification is an essential task, often constrained by the subjectivity and low efficiency of classical methods. Computer-aided automatic identification, while useful, has seldom been specifically geared toward refined lithology identification. In this study, we introduce Rock-ViT, an innovative machine learning approach. [...] Read more.
Refined lithology identification is an essential task, often constrained by the subjectivity and low efficiency of classical methods. Computer-aided automatic identification, while useful, has seldom been specifically geared toward refined lithology identification. In this study, we introduce Rock-ViT, an innovative machine learning approach. Its architecture, enhanced with supervised contrastive loss and rooted in visual Transformer principles, markedly improves accuracy in identifying complex lithological patterns. To this end, we have collected public datasets and implemented data augmentation, aiming to validate our method using sandstone as a focal point. The results demonstrate that Rock-ViT achieves superior accuracy and effectiveness in the refined lithology identification of sandstone. Rock-ViT presents a new perspective and a feasible approach for detailed lithological analysis, offering fresh insights and innovative solutions in geological analysis. Full article
(This article belongs to the Special Issue Application of Deep Learning and Computer Vision in Petrographic Images Analysis)
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