Gradient- enhanced waterpixels clustering for coal gangue image segmentation

Fu, Chenglong; Lu, Fengli; Zhang, Guoying

doi:10.1080/19392699.2022.2074409

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…Depending on the different frequency ranges of the electromagnetic waves, ground-penetrating radar [25][26][27], terahertz signals [28,29], and electron resonance identification [30] are additional modalities of detection. In the context of coal caving, numerous scholars have introduced and experimented with various techniques involving radiation [31], visuals [32,33], vibrations [34,35], sounds [36], and infrared spectroscopy recognition [37][38][39] to accurately identify the realtime caving status of coal and gangue. These technological principles have provided valuable experience for the automated control of top coal caving in LTCC mining.…”

Section: Introductionmentioning

confidence: 99%

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

Huo,

Zhao,

Zhu

et al. 2024

Sustainability

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China has made notable advancements in the intelligent construction of coal mines. However, for longwall top coal caving (LTCC) mining faces, a key obstacle impeding the intelligent transition of the coal-cutting process is automated control. This paper focuses on the aforementioned issue and comprehensively considers the pre-, intra-, and post-coal-caving stages. In this work, diverse detection and monitoring technologies are integrated at various stages through a computer platform, facilitating the construction of an automated coal caving control system with self-perception, self-learning, self-decision-making, and self-execution capabilities. Key technologies include ground-penetrating radar-based top coal thickness detection, inertial navigation-based shearer positioning, tail beam vibration-based identification of coal and gangue, and magnetostrictive sensor-based monitoring of the tail beam and insert plate attitude. In this study, the 12309 working face of the Wangjialing Coal Mine was experimentally validated, and the efficacy of the aforementioned key technologies was assessed. The results demonstrated that the control requirements for automated coal caving are satisfied by the maximum errors. Automatic regulation of coal caving was realized through the implementation of this system, thereby facilitating initiation and cessation and yielding promising experimental outcomes. Overall, this system offers practical insights for intelligent construction in current LTCC mining faces and the sustainable development of coal resources.

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Section: Introductionmentioning

confidence: 99%

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

Huo,

Zhao,

Zhu

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…However, these methods face challenges in terms of computational resource consumption, which may not align with engineering implementation needs. Additionally, the reconstructed images often exhibit excessive smoothness and lack texture information, which is crucial for coal and gangue identification 16 . Balancing image detail preservation and computational resource optimization is essential for cyclic network implementation, particularly when aiming to improve recognition accuracy and deploy the model in production environments with limited computing power.…”

Section: Introductionmentioning

confidence: 99%

Spectral band selection and ANIMR-GAN for high-performance multispectral coal gangue classification

Wang,

Hua,

Tao

et al. 2024

Sci Rep

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Low-energy and efficient coal gangue sorting is crucial for environmental protection. Multispectral imaging (MSI) has emerged as a promising technology in this domain. This work addresses the challenge of low resolution and poor recognition performance in underground MSI equipment. We propose an attention-based multi-level residual network (ANIMR) within a super-resolution reconstruction model (ANIMR-GAN) inspired by CycleGAN. This model incorporates improvements to the discriminator and loss function. We trained the model on 600 coal and gangue MSI samples and validated it on an independent set of 120 samples. The ANIMR-GAN, combined with a random forest classifier, achieved a maximum accuracy of 97.78% and an average accuracy of 93.72%. Furthermore, the study identifies the 959.37 nm band as optimal for coal and gangue classification. Compared to existing super-resolution methods, ANIMR-GAN offers advantages, paving the way for intelligent and efficient coal gangue sorting, ultimately promoting advancements in sustainable mineral processing.

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“…He et al 22 proposed pit detection and segmentation algorithms to solve the target sticking and overlapping problems, design open‐loop and closed‐loop crossover algorithms, use conjugate line to detect pits to determine the position and distance of pixel points relative to the conjugate line, and then set the distance constraints to get the segmentation line corresponding to the pits using the minimum distance search method to realize coal and gangue segmentation. Fu et al 23 proposed a fast clustering segmentation algorithm for water pixels based on gradient enhancement, which enhances the edge gradient features by multiscale details, then reconstructs the gradient watershed transform based on multiscale morphology, and finally performs statistics and clustering on the obtained hyperpixel map to get the final effect map. Liu et al 24 introduced the InceptionV1 module to replace some of these convolutional blocks based on U‐net and integrated the CPAM attention module to solve the light change problem, which improved the segmentation accuracy based on the original model.…”

Section: Introductionmentioning

confidence: 99%

Research on coal and gangue segmentation based on MFCCM‐Mask R‐CNN

Cao,

Li,

Fang

et al. 2024

Energy Science & Engineering

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Intelligent sorting of coal and gangue is of great significance to the intelligent construction of coal mines as well as green development. In this study, we propose a coal and gangue segmentation method with an improved classical segmentation network Mask R‐CNN, denoted as Multichannel Forward‐Linked Confusion Convolution Module (MFCCM)‐Mask R‐CNN. First, we design a MFCCM to construct the feature extraction network by stacking, second, we design a multiscale high‐resolution feature pyramid network structure to realize multipath fusion of feature information to enhance the position and contour information of the target, and finally, we propose a multiscale Mask head to enhance the diversity of information, and capture the more representative and unique features. Training and testing models using self‐built RGB coal and gangue data sets, the accuracy of the improved algorithm reaches 97.38%, which is an improvement of 1.66% compared to the original model. Compared with other segmentation models Unet, Deeplab V3+, Yoloact, Yolov7, and the model after replacing the backbone network, the MFCCM‐Mask R‐CNN has higher precision and recall, and can more accurately realize the efficient segmentation of coal and gangue.

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Gradient- enhanced waterpixels clustering for coal gangue image segmentation

Cited by 12 publications

References 21 publications

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

Spectral band selection and ANIMR-GAN for high-performance multispectral coal gangue classification

Research on coal and gangue segmentation based on MFCCM‐Mask R‐CNN

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