A new weakly supervised learning approach for real-time iron ore feed load estimation

作者：

Highlights：

• First attempt using image-based methods for iron ore load estimation.

• The problem is modelled as a weakly supervised learning problem.

• A new two-stage modelling for training neural networks are proposed.

• Empirical evidence of experiments shows good performance and economic boost.

摘要

•First attempt using image-based methods for iron ore load estimation.•The problem is modelled as a weakly supervised learning problem.•A new two-stage modelling for training neural networks are proposed.•Empirical evidence of experiments shows good performance and economic boost.

论文关键词：Mineral processing,Deep learning,Iron ore image processing,Weakly supervised learning

论文评审过程：Received 21 October 2021, Revised 27 April 2022, Accepted 28 April 2022, Available online 2 May 2022, Version of Record 9 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117469