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Quality Defect Detection of Crystal Drawing Process Based on Improved YOLOv8n
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Di ZHANG1, An-liang ZHOU1, Meng WEN2, Yi DU1, Xi LIU3, *
Science Technology and Engineering | 2025, 25(3) : 969 - 976
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Science Technology and Engineering | 2025, 25(3): 969-976
• Papers·General Industrial Technology •
Quality Defect Detection of Crystal Drawing Process Based on Improved YOLOv8n
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Di ZHANG1, An-liang ZHOU1, Meng WEN2, Yi DU1, Xi LIU3, *
Affiliations
  • 1. School of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
  • 2. Chengdu Zhongguang Ruihua Technology Co, Chengdu 610000, China
  • 3. State Grid Digital Technology Holdings Co., Ltd, Beijing 102600, China
Published: 2025-01-28 doi: 10.12404/j.issn.1671-1815.2402517
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During the production of monocrystalline silicon, defects generated during the crystal pulling process are recognized to severely impact product quality. Traditional visual-based defect detection methods, when applied to the detection of small protrusions in crystal pulling images, are confronted with challenges such as slow detection speeds, large parameter volumes, and difficulties in deployment on embedded terminals. In response to these challenges, an improved YOLOv8 object detection model was proposed incorporating a ContextGuided module to enhance the inference efficiency of the model. An efficient DySample was introduced into the feature fusion network to optimize the efficiency and depth of feature fusion. A lightweight network structure was adopted to reduce the complexity and computational demands of the model, making it suitable for devices with limited computing resources. The model has been trained and tested on an industrial dataset, demonstrating a more accurate detection of small protrusions with a mean average precision (mAP) of 97.7%. Compared to YOLOv8n, it exhibits an increase of 11.6% in precision and a reduction in parameter volume by 31.9%, facilitating its deployment on embedded terminals.

defect detection  /  YOLOv8  /  crystal pulling process  /  sampling operator DySample  /  ContextGuided module
Di ZHANG, An-liang ZHOU, Meng WEN, Yi DU, Xi LIU. Quality Defect Detection of Crystal Drawing Process Based on Improved YOLOv8n[J]. Science Technology and Engineering, 2025 , 25 (3) : 969 -976 . DOI: 10.12404/j.issn.1671-1815.2402517
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Article Info
doi: 10.12404/j.issn.1671-1815.2402517
  • Receive Date：2024-04-08
  • Online Date：2025-07-29
  • Published：2025-01-28
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  • Received：2024-04-08
  • Revised：2024-07-18
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Affiliations
    1. School of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
    2. Chengdu Zhongguang Ruihua Technology Co, Chengdu 610000, China
    3. State Grid Digital Technology Holdings Co., Ltd, Beijing 102600, 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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