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Image Segmentation Based on Improved U-Net Model for Beneficiation Product Zone in Dry Magnetic Separation
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Shimei LIU, Jingfeng XIAO, Yang LIU, Yong HUANG, Shengwang XIAO, Shengguang ZHANG
Mining and Metallurgical Engineering | 2024, 44(6) : 41 - 45
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Mining and Metallurgical Engineering | 2024, 44(6): 41-45
• MINERAL PROCESSING •
Image Segmentation Based on Improved U-Net Model for Beneficiation Product Zone in Dry Magnetic Separation
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Shimei LIU, Jingfeng XIAO, Yang LIU, Yong HUANG, Shengwang XIAO, Shengguang ZHANG
Affiliations
  • Changsha Research Institute of Mining and Metallurgy Co, Ltd, Changsha 410012, Hunan, China
Published: 2024-12-01 doi: 10.3969/j.issn.0253-6099.2024.06.009
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Aiming at uncertainty of beneficiation product zone in dry magnetic separation process, an image segmentation method based on an improved U-Net model was proposed by employing machine vision. In this improved model, convolutional block attention module (CBAM) is utilized to enhance the recognition and attention of the network for target areas, which is beneficial to the segmentation of target objects under complex backgrounds; depth-wise separable convolution is adopted to reduce computational complexity while maintaining accuracy, providing strong support for obtaining high-resolution images of beneficiation product zone. Thus, this model can be applied in magnetic separation and also improve network performance. It is found that this improved model can bring segmentation accuracy up to 92.28%, and also is superior to classic U-Net, DeepLabV3+ and PSPNet models in terms of contour extraction completeness and denoising capabilities.

dry magnetic separation  /  image recognition  /  image segmentation  /  machine vision  /  U-Net  /  convolutional block attention module (CBAM)  /  depth-wise separable convolution
Shimei LIU, Jingfeng XIAO, Yang LIU, Yong HUANG, Shengwang XIAO, Shengguang ZHANG. Image Segmentation Based on Improved U-Net Model for Beneficiation Product Zone in Dry Magnetic Separation[J]. Mining and Metallurgical Engineering, 2024 , 44 (6) : 41 -45 . DOI: 10.3969/j.issn.0253-6099.2024.06.009
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Year 2024 volume 44 Issue 6
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doi: 10.3969/j.issn.0253-6099.2024.06.009
  • Receive Date：2024-05-25
  • Online Date：2026-03-19
  • Published：2024-12-01
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  • Received：2024-05-25
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    Changsha Research Institute of Mining and Metallurgy Co, Ltd, Changsha 410012, Hunan, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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
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