Macroscopic Rock Texture Image Classification Using an Hierarchical Neuro-Fuzzy System

Gonçalves, Laercio B; Leta, Fabiana Rodrigues; Valente, Sérgio de Castro

doi:10.1109/iwssip.2009.5367745

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…The accuracy of the algorithm was as high as 95% on the validation set of the Googlenet architecture, but the model requires more resources in the calculation, and is not the best solution for the geological survey field. Although these studies have reduced labor cost problem and strong subjectivity that occurs in the microscopic observation method based on rock thin section images [12], [19], [11], [20], and even provide a solution for rock identification in the geological survey field, they still lack comparison with other CNN models. Therefore, how to provide a more complete solution for geological survey personnel to quickly and accurately obtain rock lithology information is still an urgent problem to be solved.…”

Section: Realized Thementioning

confidence: 99%

Recognizing Multiple Types of Rocks Quickly and Accurately Based on Lightweight CNNs Model

Fan

Chen

et al. 2020

IEEE Access

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The recognition and classification of rock lithology is an extremely important task of geological surveys. This paper proposes a new method for quickly identifying multiple types of rocks suitable for geological survey work field. Based on the two lightweight convolutional neural networks (CNNs), SqueezeNet and MobileNets, and combined with the transfer learning method, a rock lithology recognition model was established. The model was embedded into a smart phone for testing. This method was used to identify and classify the images of 28 kinds of rocks. Through a comprehensive comparison of the two models, the accuracy of SqueezeNet and MobileNets in the test dataset is 94.55% and 93.27%, respectively. Via the two models, the average recognition time of a single rock image is 557 and 836 milliseconds, and rock images with a recognition accuracy of over 96% accounted for 95% and 93% of the entire test dataset. Compared with the classification method based on rock thin section images, this method does not need to make rock thin sections. This paper meets the requirements of workers to quickly and accurately identify rocks in the work field, which improves the work efficiency and limits identification costs. INDEX TERMS Geological survey, lightweight convolutional neural networks, multiple types, rock recognition.

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Section: Realized Thementioning

confidence: 99%

Recognizing Multiple Types of Rocks Quickly and Accurately Based on Lightweight CNNs Model

Fan

Chen

et al. 2020

IEEE Access

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“…The segmentation method, on the other hand, uses the constrained automated seeded region growing technique [32]. A hierarchical neuro-fuzzy model for classification of macroscopic rock texture has been proposed in [33]. Color features have been widely used in [34] by Obara, where space transformation from RGB to CIElab was proposed.…”

Section: Related Workmentioning

confidence: 99%

Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System

Budzan

Buchczik

Pawełczyk

et al. 2019

Sensors

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This paper presents a machine vision method for detection and classification of copper ore grains. We proposed a new method that combines both seeded regions growing segmentation and edge detection, where region growing is limited only to grain boundaries. First, a 2D Fast Fourier Transform (2DFFT) and Gray-Level Co-occurrence Matrix (GLCM) are calculated to improve the detection results and processing time by eliminating poor quality samples. Next, detection of copper ore grains is performed, based on region growing, improved by the first and second derivatives with a modified Niblack’s theory and a threshold selection method. Finally, all the detected grains are characterized by a set of shape features, which are used to classify the grains into separate fractions. The efficiency of the algorithm was evaluated with real copper ore samples of known granularity. The proposed method generates information on different granularity fractions at a time with a number of grain shape features.

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Festra: A Feature Based Microscopic Sandstone Images Classification Method Using Transfer Learning

Wang

et al. 2016

Communications in Computer and Information Science

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Macroscopic Rock Texture Image Classification Using an Hierarchical Neuro-Fuzzy System

Cited by 6 publications

References 17 publications

Recognizing Multiple Types of Rocks Quickly and Accurately Based on Lightweight CNNs Model

Recognizing Multiple Types of Rocks Quickly and Accurately Based on Lightweight CNNs Model

Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System

Festra: A Feature Based Microscopic Sandstone Images Classification Method Using Transfer Learning

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