The Task of Instance Segmentation of Mineral Grains in Digital Images of Rock Samples (Thin Sections)

Bukharev, Alexander; Budennyy, Semen; Lokhanova, O.; Belozerov, Boris; Zhukovskaya, E. A.

doi:10.1109/ic-aiai.2018.8674449

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…Four Deep Learning models that generated from the training process would be inferenced through the test dataset. The value of Average Precision from each models calculated using this equation [3]: We train for total 100 epochs with learning rate of 10 -3 for the first of 40 epochs, and ending with learning rate of 10 -4 for the rest of 60 epochs later. Likewise, we adjust to train head layers for the first of 40 epochs and then for the next 60 epochs we train all layers.…”

Section: Methodsmentioning

confidence: 99%

“…Samples for dataset were taken at a microscope rotation angle of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, with a resolution of 300×250 pixels and trained using machine learning on mineral colour and texture components. Bukharev et al [3] conducted instance segmentation work on thin section images of sandstone using LinkNet and FCNN. In this study, 9000 individual grains in sandstone samples manually segmented, and then trained using FCNN to produce a model that localize object and predict binary mask.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Mask R-CNN for rock-forming minerals identification on petrography, case study at Monterado, West Kalimantan

2020

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This paper explores the experiment of Deep Learning method using Mask Region-Convolutional Neural Network (Mask R-CNN) to identify rock-forming minerals on thin section images from petrographic observation in igneous rocks, which are plagioclase, quartz, K-feldspar, pyroxene, and hornblende. Train and validation dataset consisted of 2 quartz diorites and 1 granodiorite from Monterado, West Kalimantan, 1 quartz diorite and 1 granite from Nangapinoh, West Kalimantan, and 7 andesite and 2 basalts from Bangli, Bali, while test dataset consisted of 3 quartz diorites from Monterado, West Kalimantan. This study uses 4 Mask R-CNN models, which is influenced by the lighting on polarizing microscope and using ResNet-50 architecture (Model A) or ResNet-101 (Model B), and the models that is not affected by the lighting on polarizing microscope and using ResNet-50 architecture (Model C) or ResNet-101 (Model D). From Average Precision scores, it was found that Model B has the highest score (58.0%), followed by Model A (57.8%), Model C (45.8%), and Model D (43.6%). In conclusion, the lighting of polarizing microscope is a major factor to give a better performances of Mask R-CNN models by 12%-14.4%, while the type of backbone architecture on Mask R-CNN models was not too consequential.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Mask R-CNN for rock-forming minerals identification on petrography, case study at Monterado, West Kalimantan

2020

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“…Brebandere et al [23] state that proposal-based instance segmentation methods have difficulties to extract the segmentation mask if the proposed region contains more than one instance. Bhukarev et al [24] propose a different concept for instance segmentation of densely packed mineral grains of sandstone. In the first step, region proposals are predicted using a Fully Convolutional Network (FCN) [25] to extract the grain centers from the image.…”

Section: Instance Segmentation -Proposal-basedmentioning

confidence: 99%

Multiscale Attention-Based Instance Segmentation for Measuring Crystals With Large Size Variation

Neubauer,

Berg,

Wimmer

et al. 2024

IEEE Trans. Instrum. Meas.

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Quantitative measurement of crystals in highresolution images allows for important insights into underlying material characteristics. Deep learning has shown great progress in vision-based automatic crystal size measurement, but current instance segmentation methods reach their limits with images that have large variation in crystal size or hard to detect crystal boundaries. Even small image segmentation errors, such as incorrectly fused or separated segments, can significantly lower the accuracy of the measured results. Instead of improving the existing pixel-wise boundary segmentation methods, we propose to use an instance-based segmentation method, which gives more robust segmentation results to improve measurement accuracy. Our novel method enhances flow maps with a size-aware multiscale attention module. The attention module adaptively fuses information from multiple scales and focuses on the most relevant scale for each segmented image area. We demonstrate that our proposed attention fusion strategy outperforms state-of-the-art instance and boundary segmentation methods, as well as simple average fusion of multi-scale predictions. We evaluate our method on a refractory raw material dataset of high-resolution images with large variation in crystal size and show that our model can be used to calculate the crystal size more accurately than existing methods.

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“…In practice, fuzzy boundary division and adhesion/overlap between adjacent grains are unavoidable. Accordingly, Bukharev et al [ 122 ] designed an optimization model based on a cascade of two fully convolutional neural networks (FCNN) to locate the object and predict binary masks in specific marks, or to mitigate grain coincidence through hierarchical sampling. [ 123 ]…”

Section: Single‐modal Recognition Of Mineral/rock Datamentioning

confidence: 99%

Application of Image Sensing System in Mineral/Rock Identification: Sensing Mode and Information Process

Zhang,

Yang,

Sun

et al. 2023

Advanced Intelligent Systems

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Artificial intelligence promotes the development of mineral/rock automatic recognition technology to reduce labor costs and reliance on personal experience. With instrument digitization, image recognition has played an increasingly important role. This review focuses on the image sensing system applied to mineral/rock identification and redivides this system into a sensing module, an imaging module, and an information processing module. The sensing and imaging mechanisms are summarized via technologies of sensing, imaging, and display. The information processing is classified into single‐modal or multimodal recognition. This review analyzes the limitations of single‐modal recognition and discusses the advantages and significance of multimodal recognition. The challenges of current mineral/rock identification are concluded, such as public database, multimodal learning, and portable sensing system. The interdisciplinary sensing technologies are discussed, and application feasibility is explored. This review aims to provide a reference for the research community in automatic mineral/rock identification and sensing system optimization.

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The Task of Instance Segmentation of Mineral Grains in Digital Images of Rock Samples (Thin Sections)

Cited by 12 publications

References 8 publications

Mask R-CNN for rock-forming minerals identification on petrography, case study at Monterado, West Kalimantan

Mask R-CNN for rock-forming minerals identification on petrography, case study at Monterado, West Kalimantan

Multiscale Attention-Based Instance Segmentation for Measuring Crystals With Large Size Variation

Application of Image Sensing System in Mineral/Rock Identification: Sensing Mode and Information Process

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