(Retracted) Feature extraction method CNDFA for target contour of coal and gangue based on multifractal

Li, Na; Xue, Jiameng; Gao, Sheng

doi:10.1117/1.jei.31.4.041217

Cited by 5 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of each module and the experimental results are shown in Table 2 . The average accuracy of the model (mAP@0.5), the number of parameters (Params), and the processor rate (GFLOPs) are used as the main evaluation indicators [ 25 , 26 ]. It can be seen from the table that the mAP@0.5 of the original YOLOv7-tiny network on the dataset of this paper is 92.0%, the model parameter is 6.03 M, and the calculation amount is 13.28 G. After adding the coordinate attention mechanism to the two feature maps in the middle of the backbone network, the mAP@0.5 increased by 0.4%, indicating that the coordinate attention mechanism can better extract coordinate and channel information and improve the feature expression ability of the model.…”

Section: Experimental Analysismentioning

confidence: 99%

Research on Coal and Gangue Recognition Based on the Improved YOLOv7-Tiny Target Detection Algorithm

Sui,

Zhang,

Sun

et al. 2024

Sensors

View full text Add to dashboard Cite

The recognition technology of coal and gangue is one of the key technologies of intelligent mine construction. Aiming at the problems of the low accuracy of coal and gangue recognition models and the difficult recognition of small-target coal and gangue caused by low-illumination and high-dust environments in the coal mine working face, a coal and gangue recognition model based on the improved YOLOv7-tiny target detection algorithm is proposed. This paper proposes three model improvement methods. The coordinate attention mechanism is introduced to improve the feature expression ability of the model. The contextual transformer module is added after the spatial pyramid pooling structure to improve the feature extraction ability of the model. Based on the idea of the weighted bidirectional feature pyramid, the four branch modules in the high-efficiency layer aggregation network are weighted and cascaded to improve the recognition ability of the model for useful features. The experimental results show that the average precision mean of the improved YOLOv7-tiny model is 97.54%, and the FPS is 24.73 f·s−1. Compared with the Faster-RCNN, YOLOv3, YOLOv4, YOLOv4-VGG, YOLOv5s, YOLOv7, and YOLOv7-tiny models, the improved YOLOv7-tiny model has the highest recognition rate and the fastest recognition speed. Finally, the improved YOLOv7-tiny model is verified by field tests in coal mines, which provides an effective technical means for the accurate identification of coal and gangue.

show abstract

Section: Experimental Analysismentioning

confidence: 99%

Research on Coal and Gangue Recognition Based on the Improved YOLOv7-Tiny Target Detection Algorithm

Sui,

Zhang,

Sun

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…PSO has been widely used in image segmentation [16], path planning [17], resource allocation [18], and fault diagnosis [19] due to its simplicity in calculation and image segmentation, and fast convergence. Therefore, this paper used the PSO algorithm to solve Equation (7).…”

Section: Otsu Optimization Based On Apso Algorithmmentioning

confidence: 99%

“…As for the automatic separation of coal and gangue, the most important thing is to determine the type and the location information of coal and gangue on the belt [5][6][7]. The location information accuracy of target directly affects the accuracy of discharging refuse, but the detectability of the small coal and gangue is poor due to the fewer number of pixels and texture information in coal and gangue dual-energy X-ray images [8].…”

mentioning

confidence: 99%

Small targets detection in dual-energy X-ray images of coal and gangue based on APSO-C_Otsu

Zhao

Wang

Guo

et al. 2022

Preprint

View full text Add to dashboard Cite

The positioning accuracy of coal and gangue is related to the discharge accuracy of gangue, which will affect the utilization rate of coal. But the detectability of the small coal and gangue is poor due to the fewer number of pixels and texture information in coal and gangue dual-energy X-ray images. So, the Otsu with crotch structure based on Adaptive Partical Swarm Optimization (APSO) for small targets detection is proposed, called after APSO-C_Otsu. Firstly, the Otsu with crotch structure is used to perform multi-threshold segmentation of coal and gangue dual-energy X-ray images to increase the contrast between small target and background. Meanwhile, the APSO algorithm was used to optimize the Otsu algorithm with crotch structure in order to improve its convergence speed and reduce its calculation amount. Finally, the processed image is binarized, and the location of the target was labeled based on the bwlabel algorithm. The experimental results revealed that the APSO-C_Otsu algorithm could effectively detect the small pixel size (less than 8 × 8 pixels) of coal and gangue with a particle size of 6 ~ 30 mm, and was also applicable to the coal and gangue with the particle size larger than 30 mm, which was of great significance for accurate separation of coal and gangue and the improvement of coal utilization.

show abstract

“…Chaves et al [25] used machine vision techniques for automatic characterization of coke during pulverized coal combustion. Image processing of coal and gangue mainly focuses on feature extraction [26], machine learning [27,28], and deep learning [29,30], etc. Li et al [31] proposed a coal gangue detection and recognition algorithm based on deformable convolution YOLOv3 (DCN-YOLOv3).…”

Section: Introductionmentioning

confidence: 99%

A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine

et al. 2023

View full text Add to dashboard Cite

Identification of coal and gangue is one of the important problems in the coal industry. To improve the accuracy of coal gangue identification in the coal mining process, a coal gangue identification method based on histogram of oriented gradient (HOG) combined with local binary pattern (LBP) features and improved support vector machine (SVM) was proposed. First, according to the actual underground working environment of the mine, a machine vision platform for coal gangue identification was built and the coal gangue image acquisition experiment was carried out. Then, the images of coal and gangue were denoised by median filtering, and the coal and gangue features were extracted by using the HOG combined with LBP feature extraction algorithm, and these features were normalized and principal component analysis (PCA) reduced dimension to remove the correlation and redundancy between the features. Finally, SVM, SVM optimized by genetic algorithm (GA-SVM), SVM optimized by particle swarm optimization (PSO-SVM) algorithm, and SVM optimized by grey wolf optimization (GWO-SVM) algorithm were used as classifiers for identification and classification, respectively. The experimental results show that the GWO-SVM classification model has the highest accuracy, and the average classification accuracies were 96.49% and 94.82% of the training set and test set, respectively, which shows that grey wolf algorithm to optimize support vector machine has a good effect on classification of coal gangue images, which proves the feasibility and accuracy of the proposed method.

show abstract

(Retracted) Feature extraction method CNDFA for target contour of coal and gangue based on multifractal

Cited by 5 publications

References 16 publications

Research on Coal and Gangue Recognition Based on the Improved YOLOv7-Tiny Target Detection Algorithm

Research on Coal and Gangue Recognition Based on the Improved YOLOv7-Tiny Target Detection Algorithm

Small targets detection in dual-energy X-ray images of coal and gangue based on APSO-C_Otsu

A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine

Contact Info

Product

Resources

About