Vision-based rock-type classification of limestone using multi-class support vector machine

Chatterjee, Snehamoy

doi:10.1007/s10489-012-0391-7

Cited by 76 publications

(29 citation statements)

References 40 publications

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“…In works by Chatterjee et al (Chatterjee, 2013;Chatterjee et al, 2010Chatterjee et al, , 2008Patel et al, 2017aPatel et al, , 2017bPatel et al, , 2016 crushed rocks from different mines were analyzed. Each rock was separately segmented from the image.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutions for in-depth automated rock typing

Baraboshkin

Ismailova

Orlov

et al. 2020

Computers & Geosciences

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The description of rocks is one of the most time-consuming tasks in the everyday work of a geologist, especially when very accurate description is required. We here present a method that reduces the time needed for accurate description of rocks, enabling the geologist to work more efficiently. We describe the application of methods based on color distribution analysis and feature extraction. Then we focus on a new approach, used by us, which is based on convolutional neural networks. We used several well-known neural network architectures (AlexNet, VGG, GoogLeNet, ResNet) and made a comparison of their performance. The precision of the algorithms is up to 95% on the validation set with GoogLeNet architecture. The best of the proposed algorithms can describe 50 m of full-size core in one minute.

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Section: Introductionmentioning

confidence: 99%

Deep convolutions for in-depth automated rock typing

Baraboshkin

Ismailova

Orlov

et al. 2020

Computers & Geosciences

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“…The amount of work that has been done in the area of rock segmentation on a moving conveyor belt is plentiful [2][3][4][5][6][7]. This ranges from the first image-based analysis in 1976 [8] to more recent developments utilizing laser triangulation for capturing 3D surface data.…”

Section: Previous Workmentioning

confidence: 99%

Fast inspection for size-based analysis in aggregate processing

Christie

Kochersberger

Abbott

2014

Machine Vision and Applications

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As rocks are transported along the conveyor belt of a quarry, the maximum dimension of the rocks exiting the crushers should not exceed a size threshold specific to each crusher. If the rocks are too large then they can pose a threat to equipment, and lead to a large cost in repair and loss of production. A 2D vision system is presented, which is capable of estimating the size distribution of the rocks, and also monitoring for oversize rocks. Image segmentation is performed, which is followed by a process that classifies the segments as valid or invalid using a support vector machine. A novel split algorithm is presented, which attempts to split segments that have resulted in undersegmentation. This allows the system to constantly monitor for oversize rocks without stopping the conveyor belt. For the experiments presented in this paper, a set of images was taken of rocks on a moving conveyor. In testing, it was found that 81.31 % of the segments output by the system correctly found the maximum dimension of the rock that it represented.

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“…The textural features of the rock assist in identifying its structure [1] and thus aid classification. The colors, grain sizes, and textural properties of rocks vary markedly between different rock types, allowing a basis for distinguishing them [2]. However, the accurate identification of rock type remains challenging because of the diversity of rock types and the heterogeneity of their properties [3] as well as further limitations imposed by the experience and skill of geologists [4].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed approach can identify six common rock types with an overall classification accuracy of 97.96%, thus outperforming other established deep-learning models and a linear model. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field.Mathematics 2019, 7, 755 2 of 16 allowing a basis for distinguishing them [2]. However, the accurate identification of rock type remains challenging because of the diversity of rock types and the heterogeneity of their properties [3] as well as further limitations imposed by the experience and skill of geologists [4].…”

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confidence: 99%

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Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network

et al. 2019

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The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. Deep learning is receiving significant research attention for pattern recognition and machine learning. Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. The proposed approach can identify six common rock types with an overall classification accuracy of 97.96%, thus outperforming other established deep-learning models and a linear model. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field.

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Vision-based rock-type classification of limestone using multi-class support vector machine

Cited by 76 publications

References 40 publications

Deep convolutions for in-depth automated rock typing

Deep convolutions for in-depth automated rock typing

Fast inspection for size-based analysis in aggregate processing

Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network

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