@ARTICLE{Jing_Hongdi_Research_2024,
 author={Jing, Hongdi and He, Wenxuan and Yu, Miao and Li, Xin and Zhang, Xingfan and Liu, Xiaosong and Cui, Yang and Wang, Zhijian},
 volume={vol. 69},
 number={No 3},
 journal={Archives of Mining Sciences},
 pages={447-459},
 howpublished={online},
 year={2024},
 publisher={Committee of Mining PAS},
 abstract={The degree of ore fragmentation in mining sites is closely related to crushing efficiency, equipment safety, beneficiation efficiency, and mining costs. Aiming to address the challenges of high labour intensity and low accuracy during manual ore fragmentation measurement at the mine site, this paper proposes a method for ore fragmentation recognition based on deep learning. This method not only uses the residual neural network structure to form the backbone feature extraction network of CSPDarkNet21 under the Darknet framework but also selects the simple two-way fusion feature PANet as the feature extraction network under the condition of only needing to identify large ore. PANet is simplified from three feature layers to one feature layer, which speeds up model training and prediction. The research results show that with a 6% decrease in accuracy, the model training time is reduced by 13 times, and the model running efficiency is improved by 21.2 times, significantly shortening the model development time. At the same time, CIOU calculates the loss value to make model training more stable. After the ore identification is completed, the real size of the ore can be obtained by calculating the pixel area of the prediction frame using the ore fragmentation judgement method.},
 type={Article},
 title={Research on Ore Fragmentation Recognition Method Based on Deep Learning},
 URL={http://ochroma.man.poznan.pl/Content/132709/PDF-MASTER/Archiwum-69-3-06-Hongdi%20Jing.pdf},
 doi={10.24425/ams.2024.151445},
 keywords={underground mines, ore fragmentation, visual identity, recognition, deep Learning},
}