Details

Title

Research on Ore Fragmentation Recognition Method Based on Deep Learning

Journal title

Archives of Mining Sciences

Yearbook

2024

Volume

vol. 69

Issue

No 3

Authors

Affiliation

Jing, Hongdi : Chinese Academy of Sciences,Shenya ng Institute of Automat ion, Shenya ng 110016, China ; Jing, Hongdi : Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Shenyang 110169, China ; He, Wenxuan : Ansteel Group Mining Corporat ion Limited, Anshan 114001, China ; Yu, Miao : Chinese Academy of Sciences,Shenya ng Institute of Automat ion, Shenya ng 110016, China ; Yu, Miao : Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Shenyang 110169, China ; Li, Xin : Chinese Academy of Sciences,Shenya ng Institute of Automat ion, Shenya ng 110016, China ; Li, Xin : Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Shenyang 110169, China ; Zhang, Xingfan : Chinese Academy of Sciences,Shenya ng Institute of Automat ion, Shenya ng 110016, China ; Zhang, Xingfan : Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Shenyang 110169, China ; Liu, Xiaosong : Chinese Academy of Sciences,Shenya ng Institute of Automat ion, Shenya ng 110016, China ; Liu, Xiaosong : Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Shenyang 110169, China ; Cui, Yang : Chinese Academy of Sciences,Shenya ng Institute of Automat ion, Shenya ng 110016, China ; Cui, Yang : Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Shenyang 110169, China ; Wang, Zhijian : Chinese Academy of Sciences,Shenya ng Institute of Automat ion, Shenya ng 110016, China ; Wang, Zhijian : Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Shenyang 110169, China

Keywords

underground mines ; ore fragmentation ; visual identity ; recognition ; deep Learning

Divisions of PAS

Nauki Techniczne

Coverage

447-459

Publisher

Committee of Mining PAS

Date

26.09.2024

Type

Article

Identifier

DOI: 10.24425/ams.2024.151445
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