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Nickel Plate Surface Defect Detection Based on Improved YOLOv5
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Qinyuan TAN1, 2, Yong TANG1, 2, Yan JIN3, Meiman QIN1, 2, Wei WU1, 2
Mining and Metallurgical Engineering | 2024, 44(2) : 160 - 166
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Mining and Metallurgical Engineering | 2024, 44(2): 160-166
• MATERIALS •
Nickel Plate Surface Defect Detection Based on Improved YOLOv5
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Qinyuan TAN1, 2, Yong TANG1, 2, Yan JIN3, Meiman QIN1, 2, Wei WU1, 2
Affiliations
  • 1.Changsha Institute of Mining Research Co Ltd, Changsha 410012, Hunan, China
  • 2.National Engineering Technology Research Center of Metal Mining, Changsha 410012, Hunan, China
  • 3.Jinchuan Group Co Ltd, Jinchang 737100, Gansu, China
Published: 2024-04-01 doi: 10.3969/j.issn.0253-6099.2024.02.035
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Aiming at low intelligence in nickel plate surface defect detection, a detection method based on improved YOLOv5 was proposed. Firstly, the image-enhanced dataset of nickel plate was re-clustered by K-means++ to improve the adaptability of the anchor frame to the dataset. Secondly, the convolutional block attention module (CBAM) was added into the Backbone network to strengthen the feature recognition of interest areas and unclear targets by integration of spatial and channel information. Finally, an efficient IoU (EIoU) loss was introduced to replace the original CIoU loss during bounding box regression to effectively improve the convergence speed of regression, thereby increasing the model detection speed. The experimental results show that with the self-established dataset of nickel plate defect, the improved model, compared to Faster R-CNN, SSD, YOLOv3 and YOLOv5, has higher detection accuracy up to 81.4% on average, with detection speed reaching 61 frames per second. It is concluded that this model can not only improve detection accuracy, but also satisfy the requirements for detection speed.

surface defect  /  nickel plate  /  defect detection  /  image processing  /  image enhancement algorithm  /  YOLOv5  /  convolutional block attention module (CBAM)  /  EIoU loss  /  accuracy rate  /  average precision  /  detection speed
Qinyuan TAN, Yong TANG, Yan JIN, Meiman QIN, Wei WU. Nickel Plate Surface Defect Detection Based on Improved YOLOv5[J]. Mining and Metallurgical Engineering, 2024 , 44 (2) : 160 -166 . DOI: 10.3969/j.issn.0253-6099.2024.02.035
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Year 2024 volume 44 Issue 2
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Article Info
doi: 10.3969/j.issn.0253-6099.2024.02.035
  • Receive Date：2023-10-25
  • Online Date：2026-03-19
  • Published：2024-04-01
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  • Received：2023-10-25
Funding
Affiliations
    1.Changsha Institute of Mining Research Co Ltd, Changsha 410012, Hunan, China
    2.National Engineering Technology Research Center of Metal Mining, Changsha 410012, Hunan, China
    3.Jinchuan Group Co Ltd, Jinchang 737100, Gansu, China
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https://castjournals.cast.org.cn/joweb/kygczz/EN/10.3969/j.issn.0253-6099.2024.02.035
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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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