Adaptive image enhancement and particle size identification method based on coal and gangue

Luo, Qisheng; Wang, Shuang; Guo, Yongcun; He, Lei; Li, Xin

doi:10.1088/1361-6501/ace46b

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…In recent years, particle size analysis [18,19] has been widely used in the study of fine dust materials. The normal distribution [20], lognormal distribution [21], Fuller distribution [22] and Rosin-Rammler (RRSB) distribution [23].…”

Section: Introductionmentioning

confidence: 99%

Study on the Influence of Particle Size Distribution on the Separation of Pyrite from Coal Gangue by Jigging

Hou,

Xi,

Wang

et al. 2024

Coatings

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The presence of pyrite poses a significant impediment to the comprehensive utilization of coal gangue, which is a prevalent solid waste in industrial production. However, the current efficacy of jig separation for pyrite in fine-grade coal gangue remains unsatisfactory. To investigate the influence of particle size distribution on the jig separation of pyrite in fine-grade coal gangue, the raw material was crushed to less than 2 mm using a jaw crusher and subsequently sieved to obtain its particle size distribution curve. Upon fitting the curve, it was observed that it tends towards the Rosin-Rammler (RRSB) and Fuller distributions. Leveraging these two-parameter distribution curves, adjustments were made to determine the mass within each particle size range before conducting thorough mixing followed by jig separation. The results indicate that for fine-grade gangue particles smaller than 2 mm, the RRSB distribution with a uniformity coefficient of n = 0.85 exhibits the most effective separation, although it is comparable to the separation achieved using the size distribution of raw ore. On the other hand, employing the Fuller distribution with modulus of distribution q = 1.5 yields superior separation performance. In comparison to the raw ore, the concentrate shows an increase in sulfur (S) and iron (Fe) content by factors of 3.4 and 2.4, respectively. Furthermore, compared to the RRSB distribution, there is an increase in S and Fe content by 1.91% and 2.30%, respectively; the contents of S and Fe in tailings is 0.71% and 2.72%, which can be directly used as raw materials for coating materials. Therefore, for fine-grade coal gangue particles, jigging under the Fuller distribution demonstrates better effectiveness than under the RRSB distribution.

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Section: Introductionmentioning

confidence: 99%

Study on the Influence of Particle Size Distribution on the Separation of Pyrite from Coal Gangue by Jigging

Hou,

Xi,

Wang

et al. 2024

Coatings

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LL-VI SLAM: enhanced visual-inertial SLAM for low-light environments

Ma,

Li,

et al. 2024

Meas. Sci. Technol.

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In low-light environments, the scarcity of visual information makes feature extraction and matching challenging for traditional visual Simultaneous Localization and Mapping (SLAM) systems. Changes in ambient lighting can also reduce the accuracy and recall of loop closure detection. Most existing image enhancement methods tend to introduce noise, artifacts, and color distortions when enhancing images. To address these issues, we propose an innovative low-light visual-inertial SLAM system, named LL-VI SLAM, which integrates an image enhancement network into the front end of the SLAM system. This system consists of a learning-based low-light enhancement network and an improved visual-inertial odometry. Our low-light enhancement network, composed of a Retinex-based enhancer and a U-Net-based denoiser, enhances image brightness while mitigating the adverse effects of noise and artifacts. Additionally, we incorporate a robust Inertial Measurement Unit initialization process at the front end of the system to accurately estimate gyroscope biases and improve rotational estimation accuracy. Experimental results demonstrate that LL-VI SLAM outperforms existing methods on three datasets, namely LOLv1, ETH3D, and TUM VI, as well as in real-world scenarios. Our approach achieves a Peak Signal-to-Noise Ratio of 22.08 dB. Moreover, on the TUM VI dataset, our system reduces localization error by 33.3\% compared to ORB-SLAM3, proving the accuracy and robustness of the proposed method in low-light environments.

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Micro-target localization algorithm based on improved local contour extraction and feature point matching

Li,

Guo,

Zhang

et al. 2024

Meas. Sci. Technol.

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Images at the micrometer level usually have high resolution and contain a large amount of detailed information, and traditional vision algorithms are designed for macroscopic images, making it difficult to achieve accurate target localization at the microscopic scale. In this paper, we propose a micro-target localization algorithm based on improved local contour extraction and feature point matching to address the problems of low accuracy and time-consuming operation point localization under microscopic vision due to uneven illumination, angular shift of micro-targets, and occlusion. In the horizontal perspective, a light source correction algorithm based on the morphological algorithm and an edge enhancement algorithm based on Fourier transform is proposed to improve the accuracy of threshold segmentation and edge extraction, and a contour feature extraction algorithm based on Normalized Cross-Correlation (NCC) template matching and improved Otsu's Thresholding Method is utilized to achieve high-precision localization of multi-targets in micro-scale. In the vertical perspective, a Binary Robust Invariant Scalable Keypoints (BRISK) matching algorithm based on spatial feature screening is proposed to solve the problems of feature point mismatch and inaccurate localization of traditional algorithms in case of angular offset and occlusion of micro-targets. Finally, experiments were conducted on the microscopic vision operating system and experimentally compared with cutting-edge methods to verify the feasibility and superiority of the present method. The experimental results show that the proposed algorithm in this paper has an average error of 1.023 pixels and an average elapsed time of 109.08 ms, exhibits higher stability in the presence of light source interference, angular offset, and occlusion of micro-targets, and significantly improves both localization accuracy and efficiency.

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Adaptive image enhancement and particle size identification method based on coal and gangue

Cited by 3 publications

References 42 publications

Study on the Influence of Particle Size Distribution on the Separation of Pyrite from Coal Gangue by Jigging

Study on the Influence of Particle Size Distribution on the Separation of Pyrite from Coal Gangue by Jigging

LL-VI SLAM: enhanced visual-inertial SLAM for low-light environments

Micro-target localization algorithm based on improved local contour extraction and feature point matching

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