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Deep Learning Based Algorithm and Experiment for Ceramic Small Target Defect Detection
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Wen-jie MAO, Shi-long XIE, Lin-yu-xuan LI, Xian-hai YANG*
Science Technology and Engineering | 2025, 25(11) : 4666 - 4672
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Science Technology and Engineering | 2025, 25(11): 4666-4672
• Papers·Automation and Computational Technology •
Deep Learning Based Algorithm and Experiment for Ceramic Small Target Defect Detection
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Wen-jie MAO, Shi-long XIE, Lin-yu-xuan LI, Xian-hai YANG*
Affiliations
  • School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, China
Published: 2025-04-18 doi: 10.12404/j.issn.1671-1815.2403574
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Defect detection is regarded as an indispensable step in the industrial production process. At present, manual detection is faced with the problems of low efficiency and high cost. A ceramic small target defect detection algorithm based on deep learning was proposed. For small target defects, a slice pre-training layer was first added to reduce the loss of graphics memory resources by large-size images. Secondly, a small target detection layer was added for the detection of small target defects, and a large target detection layer was removed to reduce the number of parameters. In addition, a feature selection fusion module based on MLCA (mixed local channel attention) was proposed to improve the perception of small target defects. Finally, a detection head with shared parameters was designed to further reduce the number of learnable parameters of the algorithm. By comparing with the baseline model, taking the ceramic cup as an example, the detection accuracy of this algorithm has been improved by 20.9%. Combined with the developed detection software and experimental platform, the detection efficiency of the ceramic cup has been enhanced by about 46.9%.

defect detection  /  deep learning  /  small target defects  /  ceramic cups
Wen-jie MAO, Shi-long XIE, Lin-yu-xuan LI, Xian-hai YANG. Deep Learning Based Algorithm and Experiment for Ceramic Small Target Defect Detection[J]. Science Technology and Engineering, 2025 , 25 (11) : 4666 -4672 . DOI: 10.12404/j.issn.1671-1815.2403574
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Year 2025 volume 25 Issue 11
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Article Info
doi: 10.12404/j.issn.1671-1815.2403574
  • Receive Date：2024-05-15
  • Online Date：2025-07-09
  • Published：2025-04-18
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  • Received：2024-05-15
  • Revised：2024-08-10
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    School of Mechanical Engineering, Shandong University of Technology, Zibo 255049, 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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