Research on the efficient detection method of coal gangue based on multispectral imaging and knowledge distillation strategy of SCG-YOLO.
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| Title: | Research on the efficient detection method of coal gangue based on multispectral imaging and knowledge distillation strategy of SCG-YOLO. |
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| Authors: | Yan, Pengcheng1,2,3 (AUTHOR), Wu, Zhiqi2 (AUTHOR) 1326653938@qq.com, Wen, Ziming2 (AUTHOR), Zhao, Yuanjun2 (AUTHOR), Sun, Hao2 (AUTHOR), Wang, Pinghong2 (AUTHOR) |
| Source: | International Journal of Coal Preparation & Utilization. 2026, Vol. 46 Issue 10, p3298-3326. 29p. |
| Subject Terms: | *Multispectral imaging, *Distillation, *Object recognition (Computer vision), *Principal components analysis, *Embedded computer systems, *Detection algorithms |
| Abstract: | With the aim of more effectively deploying the coal gangue detection model on embedded devices with limited resources, it is necessary to design smaller networks to reduce the number of parameters. By leveraging the complementarity of spectra across different bands, multispectral technology can fuse image information from various bands, effectively overcoming the environmental limitations of a single spectrum. This paper combines multispectral imaging technology and knowledge distillation to propose a lightweight Faster-YOLO coal gangue detection model. To begin with, multispectral imaging technology is applied to gather the spectral data of coal and gangue within diverse wavebands. Based on the spectral data of coal gangue in different bands, principal component analysis is conducted to reduce the dimensionality from 25 bands to 3 bands, and image fusion is implemented with these 3 principal component bands. Secondly, the YOLOv10n (You Only Look Once) target detection algorithm was improved. The improved SCG-YOLO was selected as the teacher model. By introducing SPD-Conv, the feature extraction ability of the network for low-resolution images was increased. A Neck structure based on CGRFPN was proposed. The enhanced SPD-Conv and CGRFPN are taken as the teacher model for knowledge distillation to diminish the model size and enhance the inference speed. Then, Faster-YOLO was selected as the student model. The network structure was made lightweight through the introduction of the FasterNet block. Through knowledge distillation, the network maintains good feature expression ability while reducing the amount of computation. The experimental results show that the Precision (P), Recall (R), and mean Average Precision (mAP) of the improved model after distillation for the recognition of coal and gangue are 97.4%, 96.1%, and 99.1%, respectively, which are 6%, 6.9%, and 2.3% higher than those of the original model, respectively. The number of parameters and the model size are 92.8% and 78.2% of those of the original model, respectively. The distilled Faster-YOLO algorithm has good detection accuracy, is lighter in weight, and is easily deployable on mobile terminals, which has a positive effect on the intelligent mining of underground coal mines. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | With the aim of more effectively deploying the coal gangue detection model on embedded devices with limited resources, it is necessary to design smaller networks to reduce the number of parameters. By leveraging the complementarity of spectra across different bands, multispectral technology can fuse image information from various bands, effectively overcoming the environmental limitations of a single spectrum. This paper combines multispectral imaging technology and knowledge distillation to propose a lightweight Faster-YOLO coal gangue detection model. To begin with, multispectral imaging technology is applied to gather the spectral data of coal and gangue within diverse wavebands. Based on the spectral data of coal gangue in different bands, principal component analysis is conducted to reduce the dimensionality from 25 bands to 3 bands, and image fusion is implemented with these 3 principal component bands. Secondly, the YOLOv10n (You Only Look Once) target detection algorithm was improved. The improved SCG-YOLO was selected as the teacher model. By introducing SPD-Conv, the feature extraction ability of the network for low-resolution images was increased. A Neck structure based on CGRFPN was proposed. The enhanced SPD-Conv and CGRFPN are taken as the teacher model for knowledge distillation to diminish the model size and enhance the inference speed. Then, Faster-YOLO was selected as the student model. The network structure was made lightweight through the introduction of the FasterNet block. Through knowledge distillation, the network maintains good feature expression ability while reducing the amount of computation. The experimental results show that the Precision (P), Recall (R), and mean Average Precision (mAP) of the improved model after distillation for the recognition of coal and gangue are 97.4%, 96.1%, and 99.1%, respectively, which are 6%, 6.9%, and 2.3% higher than those of the original model, respectively. The number of parameters and the model size are 92.8% and 78.2% of those of the original model, respectively. The distilled Faster-YOLO algorithm has good detection accuracy, is lighter in weight, and is easily deployable on mobile terminals, which has a positive effect on the intelligent mining of underground coal mines. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19392699 |
| DOI: | 10.1080/19392699.2025.2575188 |
