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<p>In the process of ore crushing, the accuracy of ore image segmentation directly affects the validity of particle size distribution detection. In order to solve the problems of low accuracy and high model complexity of existing segmentation methods, an efficient attention decoding U-Net (EU-Net) model is proposed in this article to demonstrate higher sensitivity towards subtle boundary. By focusing on the effective feature mapping of the ore and the slice-level hierarchy fusion, an efficient mapping hierarchy fusion (EffiMHF) module is designed to reduce the number of model parameters and redundant calculations. In order to solve the adhesion problems in ore image segmentation, we propose an adaptive complementary fusion segmentation (ACFS) method, which can segment more uncertain regions by relaxation prediction. Experiments show that the combination of EU-Net and ACFS can effectively solve the problem of adhesion in segmentation, making segmentation more accurate and efficient. Compared to the eight state-of-the-art segmentation models, EU-Net achieves 86.13%, 88.00% and 79.16% in metrics Accuracy, F1_score and IoU, respectively, which is the outperforming and has the least parameters.</p>

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EU-Net and ACFS: An effective method for segmenting ore images collected on-site

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月9日

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  • Shijie Zhao,
  • Yantong Zhan,
  • Weishan Niu

摘要

In the process of ore crushing, the accuracy of ore image segmentation directly affects the validity of particle size distribution detection. In order to solve the problems of low accuracy and high model complexity of existing segmentation methods, an efficient attention decoding U-Net (EU-Net) model is proposed in this article to demonstrate higher sensitivity towards subtle boundary. By focusing on the effective feature mapping of the ore and the slice-level hierarchy fusion, an efficient mapping hierarchy fusion (EffiMHF) module is designed to reduce the number of model parameters and redundant calculations. In order to solve the adhesion problems in ore image segmentation, we propose an adaptive complementary fusion segmentation (ACFS) method, which can segment more uncertain regions by relaxation prediction. Experiments show that the combination of EU-Net and ACFS can effectively solve the problem of adhesion in segmentation, making segmentation more accurate and efficient. Compared to the eight state-of-the-art segmentation models, EU-Net achieves 86.13%, 88.00% and 79.16% in metrics Accuracy, F1_score and IoU, respectively, which is the outperforming and has the least parameters.