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Multi-feature Extraction of Mineral Zone of Tabling Through Deep Semantic Segmentation

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3D Imaging—Multidimensional Signal Processing and Deep Learning

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 349))

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Abstract

Various segmentation algorithms for mineral zone images can only extract the boundary of the concentrate zone or the separation point of the mineral zone. To obtain fuller and more productive feature information from the mineral zone of Tabling’s separation, deep semantic segmentation models with DeepLab, U-net, and Xception are constructed. The image datasets of the industrial Tabling separation are collected and marked, and the corresponding mineral zone image dataset is constructed, the training and test sets are distributed in a certain proportion and imported into the deep semantic models for training. The training results of these models are compared, and the segmentation of the mineral zone images is evaluated. DeepLab-xception and DeepLab v3+ have the highest accuracy 0.9943 and mean intersection over the union value of 0.989. Finally, the DeepLab v3+ is adopted as the model for the image feature segmentation of Tabling’s mineral zone. Through the corresponding image processing and feature extraction operators, the effective multi-scale features of Tabling’s mineral zone can be well extracted.

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Acknowledgements

The authors are grateful for the experimental platform and technical assistance provided by some institutions and corporations, we extend their sincere gratitude to Jiangxi province K&R development project and fund project from Talent Project for various help and understanding throughout his work.

Fund Project

Innovative talent project: Jiangxi province “double thousand plan” (JXSQ2018101046); Jiangxi Province K&R Development Project (20212BBE53026).

Author information

Authors and Affiliations

  1. Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Huizhong Liu & Keshun You

  2. Research Center of Jiangxi Mining and Metallurgical Mechanical and Electrical Engineering Technology, Ganzhou, 341000, China

    Huizhong Liu

Authors
  1. Huizhong Liu
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  2. Keshun You
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Corresponding author

Correspondence to Huizhong Liu .

Editor information

Editors and Affiliations

  1. Interscience Institute of Management and Technology, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  2. Technical University of Sofia, Sofia, Bulgaria

    Roumen Kountchev

  3. Department of Physics, Yunnan Normal University, Kunming, China

    Yonghang Tai

  4. TK Engineering, Sofia, Bulgaria

    Roumiana Kountcheva

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Liu, H., You, K. (2023). Multi-feature Extraction of Mineral Zone of Tabling Through Deep Semantic Segmentation. In: Patnaik, S., Kountchev, R., Tai, Y., Kountcheva, R. (eds) 3D Imaging—Multidimensional Signal Processing and Deep Learning. Smart Innovation, Systems and Technologies, vol 349. Springer, Singapore. https://doi.org/10.1007/978-981-99-1230-8_5

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