收藏切换
Research on the Extraction of Separation Point Positions for Shaking Table Concentrate Band Based on Lightweight Fusion Network
收藏切换
PDF
Huizhong LIU1, 2, Jianye LIU1, Ao HUANG1, Fulong DENG1, Xixi LIU1
Mining Research and Development | 2025, 45(10) : 199 - 206
Less
收藏切换
Mining Research and Development | 2025, 45(10): 199-206
Research on the Extraction of Separation Point Positions for Shaking Table Concentrate Band Based on Lightweight Fusion Network
Full
Huizhong LIU1, 2, Jianye LIU1, Ao HUANG1, Fulong DENG1, Xixi LIU1
Affiliations
  • 1.School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
  • 2.Jiangxi Province Engineering Research Center for Mechanical and Electrical of Mining and Metallurgy, Ganzhou, Jiangxi 341000, China
Published: 2025-10-25
Outline
收藏切换

The existing object detection algorithms for shaking table concentrate bands have problems such as inability to balance detection accuracy and speed, high computational costs, difficulty in compressing model size, and slow inference speed. To address these problems, a lightweight fusion network for shaking tables (YC-Lightweight Net) object detection algorithm was proposed. The YC-Lightweight Net model firstly used a repetitive visual transformation network to extract features from the images of shaking table sub-banding. Then, by introducing group space convolution, multi-scale efficient cross stage fusion modules, and using skip connections, an efficient and lightweight neck network was designed. Finally, a weight based layer adaptive pruning algorithm was used to compress the model size. The experimental results show that the accuracy, recall, mean average precision, and FPS indicators of the YC-Lightweight Net model are 98.4%, 97.9%, 98.8% and 333 frame/s, respectively. The detection accuracy and speed are significantly better than those of the compared models. The number of parameters, floating-point operations, and model size after pruning are 13.9%, 15.4% and 17.5% of the original model, respectively. The pruning operation greatly reduces the computational complexity and model size of the model. The YC-Lightweight Net model has good detection accuracy and real-time performance, meeting the requirements of industrial equipment for lightweight models in shaking table mineral processing plants. The study can provide a technical support for accurate identification of separation points in mineral bands and intelligent upgrading of the shaking table mineral processing plant equipment.

Shaking table concentrate band  /  Object detection  /  Lightweight design  /  LAMP algorithm  /  Precise identification
Huizhong LIU, Jianye LIU, Ao HUANG, Fulong DENG, Xixi LIU. Research on the Extraction of Separation Point Positions for Shaking Table Concentrate Band Based on Lightweight Fusion Network[J]. Mining Research and Development, 2025 , 45 (10) : 199 -206 .
Year 2025 volume 45 Issue 10
PDF
95
45
Cite this Article
BibTeX
Article Info
  • Receive Date:2024-11-05
  • Online Date:2026-02-06
  • Published:2025-10-25
Article Data
Affiliations
History
  • Received:2024-11-05
Funding
Affiliations
    1.School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
    2.Jiangxi Province Engineering Research Center for Mechanical and Electrical of Mining and Metallurgy, Ganzhou, Jiangxi 341000, China
References
Share
https://castjournals.cast.org.cn/joweb/kyyjykf/EN/
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT