Research on coal gangue separation method based on dual energy x-ray image technology

Liu, Dong; Huang, Xuan; Ni, Huasheng; Tang, Wudi

doi:10.1117/12.2628771

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…When X-rays pass through different substances, they experience varying degrees of intensity attenuation. This attenuation follows the Lambert-Beer theorem [25].…”

Section: Principle Of Dual-energy X-ray Recognitionmentioning

confidence: 76%

Intelligent Gangue Sorting System Based on Dual-Energy X-ray and Improved YOLOv5 Algorithm

Qin,

Kou,

Han

et al. 2023

Applied Sciences

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Intelligent gangue sorting with high precision is of vital importance for improving coal quality. To tackle the challenges associated with coal gangue target detection, including algorithm performance imbalance and hardware deployment difficulties, in this paper, an intelligent gangue separation system that adopts the elevated YOLO-v5 algorithm and dual-energy X-rays is proposed. Firstly, images of dual-energy X-ray transmission coal gangue mixture under the actual operation of a coal mine were collected, and datasets for training and validation were self-constructed. Then, in the YOLOv5 backbone network, the EfficientNetv2 was used to replace the original cross stage partial darknet (CSPDarknet) to achieve the lightweight of the backbone network; in the neck, a light path aggregation network (LPAN) was designed based on PAN, and a convolutional block attention module (CBAM) was integrated into the BottleneckCSP of the feature fusion block to raise the feature acquisition capability of the network and maximize the learning effect. Subsequently, to accelerate the rate of convergence, an efficient intersection over union (EIOU) was used instead of the complete intersection over union (CIOU) loss function. Finally, to address the problem of low resolution of small targets leading to missed detection, an L2 detection head was introduced to the head section to improve the multi-scale target detection performance of the algorithm. The experimental results indicate that in comparison with YOLOv5-S, the same version of the algorithm proposed in this paper increases by 19.2% and 32.4% on mAP @.5 and mAP @.5:.95, respectively. The number of parameters decline by 51.5%, and the calculation complexity declines by 14.7%. The algorithm suggested in this article offers new ideas for the design of identification algorithms for coal gangue sorting systems, which is expected to save energy and reduce consumption, reduce labor, improve efficiency, and be more friendly to the embedded platform.

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“…When X-rays pass through different substances, they experience varying degrees of intensity attenuation. This attenuation follows the Lambert-Beer theorem [25].…”

Section: Principle Of Dual-energy X-ray Recognitionmentioning

confidence: 76%

Intelligent Gangue Sorting System Based on Dual-Energy X-ray and Improved YOLOv5 Algorithm

Qin,

Kou,

Han

et al. 2023

Applied Sciences

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“…The process requirements and narrow space environment of the underground mine are satisfied, and the effective separation of coal and gangue is realized. Dong et al [25] proposed a threshold sorting method based on dual-energy X-ray technology. The recognition rates of 150~300 mm materials were 96.99% and 92.32%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Coal–Rock Data Recognition Method Based on Spectral Dimension Transform and CBAM-VIT

Yang,

Zhang,

Wang

et al. 2024

Applied Sciences

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Coal–gangue sorting is a vital component of intelligent mine construction. As intelligent manufacturing continued to advance, data-driven coal–gangue recognition emerged as a prominent research topic. However, conventional data-driven methods for coal–gangue recognition heavily rely on expert-extracted features. The process of feature extraction is labor-intensive and significantly impacts the final outcome. Deep learning (DL) offers an effective approach to automatically extract features from raw data. Among the various DL techniques, convolutional neural networks (CNNs) have proven to be particularly effective. In this paper, we propose an intelligent method for recognizing coal–rock by fusing multiple preprocessing techniques applied to near-infrared spectra and employing dual attention. Initially, a signal-to-RGB image conversion method is applied to fuse three types of preprocessing data, namely first-order differential, second-order differential, and standard normal transform, into an RGB image representation. Subsequently, we propose a neural network model (CBAM-VIT) that integrates the convolutional block attention mechanism (CBAM) and Vision Transformer (VIT). When evaluated on the coal–rock dataset, this model achieves an accuracy of 98.5%, surpassing the performance of VIT (95.3%), VGG-16 (89%), and AlexNet (82%). The comparative results clearly demonstrate that the proposed coal–gangue recognition method yields significant improvements in classification outcomes.

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“…Recently some review articles have published in the same area [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] creating an exchange platform for cost effective engineering solutions in sensor based ore sorting. More beneficiation and advanced techniques for minerals like copper, coal, and diamond have also been proposed in [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], and [36], but none of these approaches intently reviews a generalized approach to sensor based ore sorting through the application of the interaction of electromagnetic waves with matter.…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Ore Sorting—A Review of Current Use of Electromagnetic Spectrum in Sorting

et al. 2022

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Sensor-based sorting has had a wide range of industrial use in automating and speeding up the process which requires substances or objects to be segregated from each other. The high demand for goods including raw materials, food, minerals, and waste recycling has increased the pressure for high-speed sorting. The first part of this paper presents a comprehensive theoretical and practical survey and comparison of current sorting methods relying on the use of the electromagnetic spectrum. Sorting methods are classified among other things, which portion of the EM spectrum is it, background noise rejection capacity, sample size limitations, sample chemistry limitations, sample surface cleanliness, spatial resolution capacity, spectral resolution, and feed rate limitations. The analysis focuses on color or visible light sorting, gamma-ray sorting, infra-red sorting, x-ray transmission-based sorting and x-ray fluorescence sorting, coupling the findings to the classification criteria outlined. We see a need to define a universal sorting scheme that will in general be applied to most sorting tasks. To do this, the final part of this paper re-looks at the x-ray transmission and x-ray fluorescence sorting scheme in line with the established limitations and proposes a dual x-ray transmission and fluorescence method to mitigate the challenge affecting the different schemes.

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Research on coal gangue separation method based on dual energy x-ray image technology

Cited by 4 publications

References 7 publications

Intelligent Gangue Sorting System Based on Dual-Energy X-ray and Improved YOLOv5 Algorithm

Intelligent Gangue Sorting System Based on Dual-Energy X-ray and Improved YOLOv5 Algorithm

Coal–Rock Data Recognition Method Based on Spectral Dimension Transform and CBAM-VIT

Sensor-Based Ore Sorting—A Review of Current Use of Electromagnetic Spectrum in Sorting

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