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A Focal Attention-Based Large Convolutional Kernel Network for Anomaly Detection of Coated Fuel Particles

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成果类型:
期刊论文
作者:
Hu, Zhaochuan;Yu, Jiang;Zhang, Hang;Liu, Jian;Chen, Ning;...
通讯作者:
Liu, J
作者机构:
[Chen, Ning; Li, Rong; Liu, Jian; Zhang, Hang; Hu, Zhaochuan; Yu, Jiang] Hunan Univ, State Key Lab Adv Design & Manufacture Vehicle Bod, Changsha 410082, Peoples R China.
[Zhang, Hang] Changsha Univ Sci & Technol, Coll Automot & Mech Engn, Changsha 410114, Peoples R China.
通讯机构:
[Liu, J ] H
Hunan Univ, State Key Lab Adv Design & Manufacture Vehicle Bod, Changsha 410082, Peoples R China.
语种:
英文
关键词:
coated fuel particles;image classification;focal attention;large convolutional kernel
期刊:
Sensors
ISSN:
1424-8220
年:
2025
卷:
25
期:
11
基金类别:
Science and Technology Innovation Program of Hunan Province; National Natural Science Foundation of China [52305526]; [2023GK2008]
机构署名:
本校为其他机构
院系归属:
汽车与机械工程学院
摘要:
The coating thickness of fuel particles is a critical parameter for ensuring the safe operation of high-temperature gas-cooled reactors. However, existing detection technologies still face limitations in measurement accuracy, efficiency, and automation. Notably, accurate thickness measurement relies on the precise identification of anomalous particles, which is hindered by several key challenges. First, incomplete particles in edge regions introduce significant interference. Second, some anomalies exhibit weak morphological features, making them difficult to detect. To address these issues, th...

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