Spatial Effect Analysis of Coal and Gangue Recognition Detector Based on Natural Gamma Ray Method

Zhao, Mingxin; Liu, Huaishan; Li, Xuefeng; Li, Fenglin; Yang, Xilei; Xia, Qiuping; Ma, Qing

doi:10.1007/s11053-022-10016-z

Cited by 10 publications

(6 citation statements)

References 15 publications

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“…During the tailing management stage 5 , unutilized coal can be effectively identified and recovered, thereby minimizing waste. Currently, there are various methods for coal and coal-measure rock identification, such as imaging 6 , gamma-ray 7 , and radar 8 techniques. In recent years, spectral-based methods have gained widespread attention due to their advantages in speed and efficiency 9 .…”

Section: Background and Summarymentioning

confidence: 99%

A near-infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions

Lv,

Wang,

Yang

et al. 2024

Sci Data

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The identification technology for coal and coal-measure rock is required across multiple stages of coal exploration, mining, separation, and tailings management. However, the construction of identification models necessitates substantial data support. To this end, we have established a near-infrared spectral dataset for coal and coal-measure rock, which includes the reflectance spectra of 24 different types of coal and coal-measure rock. For each type of sample, 11 sub-samples of different granularities were created, and reflectance spectra were collected from sub-samples at five different detection azimuths, 18 different detection zeniths, and under eight different light source zenith conditions. The quality and usability of the dataset were verified using quantitative regression and classification machine learning algorithms. Primarily, this dataset is used to train artificial intelligence-based models for identifying coal and coal-measure rock. Still, it can also be utilized for regression studies using the industrial analysis results contained within the dataset.

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Section: Background and Summarymentioning

confidence: 99%

A near-infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions

Lv,

Wang,

Yang

et al. 2024

Sci Data

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“…Traditional methods for gangue selection in coal processing include manual gangue selection, jigging gangue selection, heavy media gangue selection, and ray gangue selection [33]. Manual gangue separation is characterized by high labor intensity and low efficiency.…”

Section: Related Workmentioning

confidence: 99%

Improved YOLOv7 Network Model for Gangue Selection Robot for Gangue and Foreign Matter Detection in Coal

Yang

Miao

et al. 2023

Sensors

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Coal production often involves a substantial presence of gangue and foreign matter, which not only impacts the thermal properties of coal and but also leads to damage to transportation equipment. Selection robots for gangue removal have garnered attention in research. However, existing methods suffer from limitations, including slow selection speed and low recognition accuracy. To address these issues, this study proposes an improved method for detecting gangue and foreign matter in coal, utilizing a gangue selection robot with an enhanced YOLOv7 network model. The proposed approach entails the collection of coal, gangue, and foreign matter images using an industrial camera, which are then utilized to create an image dataset. The method involves reducing the number of convolution layers of the backbone, adding a small size detection layer to the head to enhance the small target detection, introducing a contextual transformer networks (COTN) module, employing a distance intersection over union (DIoU) loss border regression loss function to calculate the overlap between predicted and real frames, and incorporating a dual path attention mechanism. These enhancements culminate in the development of a novel YOLOv71 + COTN network model. Subsequently, the YOLOv71 + COTN network model was trained and evaluated using the prepared dataset. Experimental results demonstrated the superior performance of the proposed method compared to the original YOLOv7 network model. Specifically, the method exhibits a 3.97% increase in precision, a 4.4% increase in recall, and a 4.5% increase in mAP0.5. Additionally, the method reduced GPU memory consumption during runtime, enabling fast and accurate detection of gangue and foreign matter.

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“…The fully mechanized longwall top coal caving (LTCC) mining technology is one of the main technologies used for mining thick seams 4 – 7 . Improper timing of the caving-opening closure in the caving process of the LTCC mining face can result in excessive or insufficient caving, leading to resource wastage or compromising coal quality 8 , 9 . Reasonable time for closing the caving-opening depends on the mixing degree of coal gangue 10 – 12 .…”

Section: Introductionmentioning

confidence: 99%

Precise detection of coal and gangue based on natural γ-ray

Zhang,

Liu,

Zhu

et al. 2024

Sci Rep

Self Cite

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To address the technical limitations of automatic coal and gangue detection technology in fully mechanized top coal caving mining operations, the low radiation level radioactivity measurement method is utilized to assess the degree of coal-gangue mixture in top coal caving process. This approach is based on the distinguishing radiation characteristics of natural γ-rays between coal and gangue. This study analyzed the distribution characteristics of natural γ-rays in coal and rock layers of thick coal seams and the applicability of this method, introduced the basic principle of coal-gangue detection technology based on natural γ-ray, developed the test system about automatic coal-gangue detection, studied the radiation characteristics of coal and gangue, proposed determination model of the coal-gangue mixed degree, combined with the time sequence characteristics of the top coal’s releasing flow and the energy spectrum characteristics of different layers of rock, realized the precise coal-gangue detection technology in complex structure thick coal seam with multiple gangue. Field tests were conducted in Lilou, Xiaoyu and Tashan Coal Mine. The test results were well corroborated with the research results and achieved the expected results, which laid the foundation for the field application of intelligent coal mining.

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Spatial Effect Analysis of Coal and Gangue Recognition Detector Based on Natural Gamma Ray Method

Cited by 10 publications

References 15 publications

A near-infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions

A near-infrared spectroscopy dataset of coal and coal-measure rock under diverse conditions

Improved YOLOv7 Network Model for Gangue Selection Robot for Gangue and Foreign Matter Detection in Coal

Precise detection of coal and gangue based on natural γ-ray

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