Coal gangue recognition based on spectral imaging combined with XGBoost

Zhou, Minghao; Lai, Wenhao

doi:10.1371/journal.pone.0279955

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…Similarly, XGBoost could handle tedious conditions and various pharmaceutical data to meet the basic requirements of a fast time diagnosis prediction, with greater accuracy in the classification [42,43]. Heart disease was predicted using an optimized XGBoost algorithm with an accuracy of 94.7% [44,45]. A hybrid method, principal component analysis (PCA) with XGBoost, was used for the classification of coal gangue with a 98.33% accuracy rate [46,47].…”

Section: Related Work and Problem Descriptionmentioning

confidence: 99%

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Mani,

Rakkiya Goundar Komarasamy,

Rajamanickam

et al. 2024

Processes

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Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the Multi-Objective Optimization Genetic Algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification.

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Section: Related Work and Problem Descriptionmentioning

confidence: 99%

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Mani,

Rakkiya Goundar Komarasamy,

Rajamanickam

et al. 2024

Processes

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show abstract

“…The above studies have achieved remarkable results in identifying underground coal and gangue. Still, the literature [7][8][9][10][11][12][13][14][15] only uses the properties of coal and gangue to identify, which has the disadvantages of solid subjectivity, significant differences in data set collection, and incomplete consideration of characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Zhao et al 10 analyzed the difference in information contained in the EDXRD spectra of coal and gangue, extracted the identification features of coal and gangue, and realized the identification of coal and gangue using the particle swarm optimization–support vector machine (SVM) model. Zhou and Lai 11 and Hu et al 12 conducted a study on identifying coal gangue from spectral analysis. Zhou et al 13 and He et al 14 analyzed the identification of coal and gangue from the dual‐energy X‐ray R value method and dual‐energy X‐ray and dual‐vision visible light imaging, respectively.…”

Section: Introductionmentioning

confidence: 99%

A research study of lightweight state perception algorithm based on improved YOLOv5s‐Tiny for fully mechanized top‐coal caving mining

Shan,

Yang,

et al. 2024

Energy Science & Engineering

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Real‐time monitoring of the coal caving process in fully mechanized mining is crucial for achieving intelligent and efficient top‐coal caving. While the coal gangue identification method, employing vision and deep learning, has advanced in the realm of intelligent monitoring, it exhibits a dependency on high‐performance hardware. This reliance poses challenges for deploying identification equipment on mobile terminals, hindering the widespread application of this method. To address the issues above, the paper presents a lightweight algorithm, utilizing You Only Look Once version 5s (YOLOv5s), utilizing YOLOv5s for the real‐time perception of the top‐coal caving state in fully mechanized caving mining. We replace the backbone network of YOLOv5s with the ShuffleNetv2 structure that is more suitable for lightweight deployment, and add the Simple Attention Mechanism attention mechanism to the network structure to enhance the model's receptive field and feature expression ability, and reduce the impact of falling debris on the detection results. A dynamic experimental platform for top‐coal caving in fully mechanized caving mining for thick coal seams is set up, and preprocessing operations such as brightness, sharpening, and denoising are performed on the image data sets collected by high‐speed industrial cameras. Research results show that compared with the traditional YOLOv5s, the improved model's P, mAP, F1 score, and other indicators have increased by 3.4%, 2.1%, and 1.1%, respectively, the model size is 70% of the original, and the detection frames per second value has increased by 48.1%. The lightweight algorithm stabilizes the accuracy of coal gangue identification dramatically in real time. It dramatically reduces the computing pressure on the mobile terminal, providing basic theory and practice for real‐time monitoring of fully mechanized coal caving mining.

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“…Machine learning algorithms and convolutional neural network algorithms have become more and more popular. Using machine learning technology to establish a coal gangue recognition classifier, the precise classification of coal and gangue can be realized by extracting the image features of coal and gangue. , However, the machine learning method needs to manually extract various coal and gangue features, and the process is complicated, which is not conducive to the rapid identification of coal and gangue. In contrast, a convolutional neural network can automatically extract high-level features of images and respond quickly.…”

Section: Introductionmentioning

confidence: 99%

Research on the Strong Generalization of Coal Gangue Recognition Technology Based on the Image and Convolutional Neural Network under Complex Conditions

Xun,

Yang,

Liu

2023

ACS Omega

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A coal gangue image recognition method based on complex conditions is proposed to address the current issue of image-based coal gangue recognition being greatly affected by complex conditions. First, complex conditions such as different shooting backgrounds, shooting distances, and lighting intensities are set to simulate the underground coal mining environment. Then, based on three convolutional neural network algorithms, the coal gangue recognition model is established, and the influence of different complex conditions on coal gangue image recognition is analyzed. At the same time, a network model with a strong generalization ability is determined. The results show that the accuracy of coal gangue image recognition has no obvious regularity under different shooting background conditions, and complex environments should be the primary factor affecting the accuracy of coal gangue image recognition. The accuracy of coal gangue image recognition is negatively correlated with the increase in shooting distance, and strong light conditions are conducive to improving the accuracy of coal gangue image recognition. The LeNet network model has the strongest generalization ability, which can meet the requirements of recognition accuracy and respond quickly. The accuracy of coal gangue image recognition under different complex conditions can reach more than 0.99, and the average single image recognition time is only 177 ms. This article studies the influence law of different complex conditions on the recognition of coal and gangue images and confirms that the LeNet network has strong generalization ability, achieving accurate and fast recognition of coal gangue images under complex conditions and providing a reference basis for the deployment of underground coal gangue sorting.

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Coal gangue recognition based on spectral imaging combined with XGBoost

Cited by 7 publications

References 35 publications

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

A research study of lightweight state perception algorithm based on improved YOLOv5s‐Tiny for fully mechanized top‐coal caving mining

Research on the Strong Generalization of Coal Gangue Recognition Technology Based on the Image and Convolutional Neural Network under Complex Conditions

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