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

Zhang, Ningbo; Liu, Changyou; Zhu, Chuanqi; Chen, Baobao; Wang, Zhongbin; Wu, Xiaojie

doi:10.1038/s41598-024-51424-w

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…However, there are still some challenges in practical applications [1][2][3][4] . The coal gangue sorting robot identifies the target coal gangue and obtains its pose information through the recognition system during the sorting process [5][6][7] . The robot control system combines pose information, belt speed, and time to calculate the real-time position of the target coal gangue, thereby achieving robot sorting.…”

Section: Introductionmentioning

confidence: 99%

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

Ye,

Hongwei,

Peng

et al. 2024

Preprint

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The coal gangue sorting robot may encounter variations in the pose of the target coal gangue due to belt slippage, deviation, and speed fluctuations, leading to failed or missed grasping attempts during the sorting process of coal gangue. In response to this issue, we propose a special two-stage network coal gangue image fast matching method to re-obtain the target coal gangue pose information, further improving the grasping accuracy and efficiency of the robot. In the first stage, we use SuperPoint to enhance the scene adaptability and credibility of feature point extraction. The improved Multi-scale Retinex with Color Restoration enhancement algorithm is used to further enhance the ability of Superpoint to detect feature points. In the second stage, we introduce SuperGlue for feature matching to improve the robustness of the matching network. Integrating the Progressive Sample Consensus algorithm to further eliminate erroneous feature matching point pairs and improve the accuracy of image matching. We conducted matching experiments of coal gangue under different object distances, scales, and rotation angles using various methods on the double-manipulator truss-type coal gangue sorting robot experimental platform independently developed by our team. The results showed that the matching rate of the proposed method was 98.2%, with a matching time of 84.6ms. It has the characteristics of a high matching rate, good real-time performance, and strong robustness, and can meet the requirements of efficient and accurate matching between coal gangue recognition images and sorting images.

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Section: Introductionmentioning

confidence: 99%

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

Ye,

Hongwei,

Peng

et al. 2024

Preprint

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show abstract

“…Intelligent gangue selection is primarily divided into ray selection and image selection. Ray-based gangue selection is mainly divided into two categories: X-ray 11 and γ-ray 12 , which distinguish coal from gangue based on radiation attenuation levels. Image-based gangue selection is trained and optimized using deep learning algorithms, enabling the identification of coal and gangue.…”

Section: Introductionmentioning

confidence: 99%

Design and application of coal gangue sorting system based on deep learning

Zhang,

Wang,

Zhang

et al. 2024

Sci Rep

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With the advancement of science and technology, coal-washing plants are transitioning to intelligent, information-based, and professional sorting systems. This shift accelerates the construction a modern economic system characterized by green and low-carbon development, thereby promoting the high-quality advancement of the coal industry. Traditional manual gangue picking and multi-axis robotic arm gangue selection currently suffer from low recognition accuracy, slow sorting efficiency, and high worker labor intensity. This paper proposes a deep learning-based, non-contact gangue recognition and pneumatic intelligent sorting system. The system constructs a dynamic database containing key feature information such as the target gangue's contour, quality, and center of mass. The system elucidates the relationships between ejection speed, mass, volume, angle of incidence, and the impact energy matching mechanism. Demonstration experiments using the system prototype for coal gangue sorting reveal that, compared to existing robotic arm sorting methods in coal washing plants, this system achieves a gangue identification accuracy exceeding 97%, a sorting rate above 91%, and a separation time of less than 3 s from identification to separation, thereby effectively enhancing raw coal purity.

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Intelligent detection of coal gangue in mining Operations using multispectral imaging and enhanced RT-DETR algorithm for efficient sorting

Yan,

Wen,

et al. 2024

Microchemical Journal

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

Cited by 6 publications

References 35 publications

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

Research on Efficient Matching Method of Coal Gangue Recognition Image and Sorting Image

Design and application of coal gangue sorting system based on deep learning

Intelligent detection of coal gangue in mining Operations using multispectral imaging and enhanced RT-DETR algorithm for efficient sorting

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