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NUCLEAR TECHNIQUES, Volume. 48, Issue 6, 060004(2025)

A defect detection method for fuel rod welds based on imbalanced convolution feature extraction

Fan HUANG1、*, Bo XIANG1, Ping LI1, and Yue LIU2
Author Affiliations
  • 1CNNC Jianzhong Nuclear Fuel Co., Ltd, Yibin 644000, China
  • 2Harbin Institute of Technology, Harbin 150001, China
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    [1] YANG Qinke, WANG Huacai, CHENG Huanlin et al. Hardness and Young's modulus of intact and leak PWR fuel rods[J]. Atomic Energy Science and Technology, 58, 157-165(2024).

    [2] HUANG Fan, XIANG Bo, LI Ping et al. Brightness fusion and multi-scale optimized enhancement algorithm for fuel rod DR images[J]. Nuclear Techniques, 48, 240004(2025).

    [3] Naddaf-Sh M M, Naddaf-Sh S, Zargarzadeh H et al. Defect detection and classification in welding using deep learning and digital radiography[M]. Fault Diagnosis and Prognosis Techniques for Complex Engineering Systems, 327-352(2021).

    [4] WU Bo. Research on key technologies for automatic identification of internal defects in aerospace titanium alloy castings[D](2022).

    [5] ZHANG Xiaogang, YU Dongbao, TANG Hui et al. Research on automatic detection method of X-ray fuel rod end plug defects based on deep learning[J]. China Industrial Economics, 58, 1767-1776(2024).

    [6] WANG Rui, GAO Shaoze, LIU Weipeng et al. A lightweight and efficient X-ray weld image defect detection method[J]. Transactions of the China Welding Institution, 45, 41-49(2024).

    [7] Ferguson M K, Ronay A, Lee Y T et al. Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning[J]. Smart and Sustainable Manufacturing Systems, 2, 10.1520/SSMS20180033(2018).

    [8] Mery D. Aluminum casting inspection using deep object detection methods and simulated ellipsoidal defects[J]. Machine Vision and Applications, 32, 72(2021).

    [9] Lal R, Bolla B K, Sabeesh E. Efficient neural net approaches in metal casting defect detection[J]. Procedia Computer Science, 218, 1958-1967(2023).

    [10] ZHOU Zhou. Technology of wheel core defect detection and defect sample generation[D](2019).

    [11] GUO Ruiqi, WANG Mingquan, ZHANG Junsheng et al. Hub defect segmentation based on U-Net convolutional neural network[J]. Automation & Instrumentation, 35, 43-47(2020).

    [12] Yu H, Li X J, Song K C et al. Adaptive depth and receptive field selection network for defect semantic segmentation on castings X-rays[J]. NDT & E International, 116, 102345(2020).

    [13] Du W Z, Shen H Y, Fu J Z et al. Automated detection of defects with low semantic information in X-ray images based on deep learning[J]. Journal of Intelligent Manufacturing, 32, 141-156(2021).

    [14] Sohan M, Sai Ram T, Rami Reddy C V. A review on YOLOv8 and its advancements[M]. Data Intelligence and Cognitive Informatics, 529-545(2024).

    [15] Ding B D, Zhang H M, Huang Z H et al. Data enhanced YOLOv8s algorithm for X-ray weld defect detection[J]. Nondestructive Testing and Evaluation, 1-24(2024).

    [16] Wang G, Chen Y F, An P et al. UAV-YOLOv8: a small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios[J]. Sensors, 23, 7190(2023).

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    Fan HUANG, Bo XIANG, Ping LI, Yue LIU. A defect detection method for fuel rod welds based on imbalanced convolution feature extraction[J]. NUCLEAR TECHNIQUES, 2025, 48(6): 060004

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    Paper Information

    Category: Special Topics of Academic Papers at The 27th Annual Meeting of the China Association for Science and Technology

    Received: Apr. 28, 2025

    Accepted: --

    Published Online: Jul. 25, 2025

    The Author Email: Fan HUANG (879967686@qq.com)

    DOI:10.11889/j.0253-3219.2025.hjs.48.250187

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology