An Image Recognition Method for Coal Gangue Based on ASGS-CWOA and BP Neural Network

Wang, Dongxing; Ni, Jingxiu; Du, Tingyu

doi:10.3390/sym14050880

Cited by 8 publications

(12 citation statements)

References 9 publications

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“…The augmented image dataset was used for rock image classification, and the classification accuracy reached 96.38%. Compared with [9], this method generates clearer images with less redundant information, which is suitable for datasets containing multiple rock images, and improves the model accuracy while enhancing the model generalization ability. Compared with [11], the rock image data generated by this method can effectively improve the accuracy of the classification model.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, scholars have proposed some practical methods for rock image data augmentation as a way to improve the accuracy of rock image classification. Hong et al used the traditional data enhancement technique for coal gangue image data for data enhancement, which effectively improved the classification accuracy of coal gangue; the method is not applicable to the dataset containing multiple rock images, the traditional image data enhancement method generates images with little differentiation from the original image data, and there is a large amount of redundant feature information, which is not conducive to improving the generalization ability of the classification model [9]. Baraboshkin et al subjected the collected 2000 stratigraphic rock images to data augmentation to 20,000 images to improve the accuracy of the classification task and prevent overfitting, but the method in the paper was not effective in augmenting the image data, and there was a large amount of redundant feature information in the generated images [10].…”

Section: Introductionmentioning

confidence: 99%

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GAN Data Augmentation Methods in Rock Classification

Zhao

Wang

et al. 2023

Applied Sciences

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In this paper, a data augmentation method Conditional Residual Deep Convolutional Generative Adversarial Network (CRDCGAN) based on Deep Convolutional Generative Adversarial Network (DCGAN) is proposed to address the problem that the accuracy of existing image classification techniques is too low when classifying small-scale rock images. Firstly, Wasserstein distance is introduced to change the loss function, which makes the training of the network more stable; secondly, conditional information is added, and the network has the ability to generate and discriminate image data with label information; finally, the residual module is added to improve the quality of generated images. The results demonstrate that by applying CRDCGAN to the augmented rock image dataset, the accuracy of the classification model trained on this dataset is as high as 96.38%, which is 13.39% higher than that of the classification model trained on the non-augmented dataset, and 8.56% and 6.27% higher than that of the traditional dataset augmented method and the DCGAN dataset augmentation method, respectively. CRDCGAN expands the rock image dataset, which makes the rock classification model accuracy effectively improved. The data augmentation method was found to be able to change the accuracy of the classification model to a greater extent.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GAN Data Augmentation Methods in Rock Classification

Zhao

Wang

et al. 2023

Applied Sciences

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“…For coal pillar rockburst, due to many influencing factors, rockburst has different stress characteristics and energy variation laws, and it is difficult to effectively monitor the rockburst hazard by using a monitoring method. Therefore, an appropriate monitoring method should be selected based on the expected rockburst performance characteristics (Jia et al, 2014;Zhang, 2021;Wang et al, 2022;. The DNN model structure includes an input layer and several hidden layers.…”

Section: Multi-parameter Comprehensive Early Warningmentioning

confidence: 99%

“…Based on the sand-steel interface shear test, Kou et al (2021) studied the interaction mechanism between coral sand and steel pipe pile interface under the geological conditions of coral reef. Wang et al (2022) analyzed the macroscopic shear characteristics and particle crushing characteristics. For the interface of the concrete pile, Li et al (2022) carried out large-scale direct shear tests to study the shear failure characteristics of coral reef limestone-concrete interface.…”

Section: Introductionmentioning

confidence: 99%

Coal and Rock Dynamic Disasters: Advances of Physical and Numerical Simulation in Monitoring, Early Warning, and Prevention - Volume II

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…In image recognition based on machine learning, the most commonly used are back propagation (BP) neural network and support vector machine (SVM). BP neural network has the disadvantages of slow convergence speed and falling into local extremum [32]. However, support vector machine has good generalization performance and takes into account the advantages of training error minimization and test error minimization.…”

Section: Introductionmentioning

confidence: 99%

A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine

et al. 2023

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Identification of coal and gangue is one of the important problems in the coal industry. To improve the accuracy of coal gangue identification in the coal mining process, a coal gangue identification method based on histogram of oriented gradient (HOG) combined with local binary pattern (LBP) features and improved support vector machine (SVM) was proposed. First, according to the actual underground working environment of the mine, a machine vision platform for coal gangue identification was built and the coal gangue image acquisition experiment was carried out. Then, the images of coal and gangue were denoised by median filtering, and the coal and gangue features were extracted by using the HOG combined with LBP feature extraction algorithm, and these features were normalized and principal component analysis (PCA) reduced dimension to remove the correlation and redundancy between the features. Finally, SVM, SVM optimized by genetic algorithm (GA-SVM), SVM optimized by particle swarm optimization (PSO-SVM) algorithm, and SVM optimized by grey wolf optimization (GWO-SVM) algorithm were used as classifiers for identification and classification, respectively. The experimental results show that the GWO-SVM classification model has the highest accuracy, and the average classification accuracies were 96.49% and 94.82% of the training set and test set, respectively, which shows that grey wolf algorithm to optimize support vector machine has a good effect on classification of coal gangue images, which proves the feasibility and accuracy of the proposed method.

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An Image Recognition Method for Coal Gangue Based on ASGS-CWOA and BP Neural Network

Cited by 8 publications

References 9 publications

GAN Data Augmentation Methods in Rock Classification

GAN Data Augmentation Methods in Rock Classification

Coal and Rock Dynamic Disasters: Advances of Physical and Numerical Simulation in Monitoring, Early Warning, and Prevention - Volume II

A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine

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