【摘要】 核燃料棒在反应堆运行中面临极端服役环境，其包壳管与端塞焊缝的精密检测是核安全的重要保障措施。针对真实缺陷发生概率低、X射线成像设备成本高等原因造成高质量的训练数据集缺乏，以及现有模型难以满足实时性检测需求等问题，本文设计了一种基于不均衡卷积特征提取的智能缺陷检测算法，通过在边界回归损失函数中引入距离注意力机制，构建基于不均衡卷积的特征提取主干网络，获得了具有高检测精度和良好实时性的检测模型YOLOv8n-WIOU-Fasternet，解决缺陷特征不均衡导致的检测模型精度有限和稳定性差的问题。通过自行采集的500支燃料棒的X射线数字成像（Digital Radiography,DR）图像，制成一批包含气孔、气胀、未焊透、夹钨和堵孔等异常缺陷样品。选取端塞焊缝附近作为感兴趣区域，采用开源图像标注工具LabelImg对缺陷区域进行手动标注，获得720张缺陷图像。通过对该数据集进行了系统性的数据扩充，生成带有“伪缺陷”的样本，构建出包含不同类型缺陷的燃料棒DR图像共计7 200张，其中气孔、夹钨、未焊透等不同类型缺陷在训练集中的数量相同，并按8:2的比例划分为训练集和验证集。另行采集了72张未参与训练和验证过程的燃料棒缺陷图像，作为独立测试集，对本文所提出的缺陷检测模型进行了性能评估与实验验证。实验结果表明，该模型的检测精度明显优于人工特征提取类缺陷检测方法和原始的YOLOv8模型，漏检率和误检率均低于5%。本文所提出的模型在检测精度和计算效率之间实现了良好的平衡，为燃料棒缺陷检测提供了一种创新且实用的解决方案。更多还原

【Abstract】 [Background] Nuclear fuel rods operate under extreme service environments in reactor systems, hence precise inspection of cladding tube and end plug welds represents a critical nuclear safety safeguard. Traditional detection methods face significant challenges including the scarcity of high-quality training datasets due to low real defect occurrence rates and high X-ray imaging equipment costs, alongside existing models’ inability to meet realtime detection requirements. [Purpose] This study aims to develop an intelligent weld defect detection algorithm based on imbalanced convolution feature extraction that achieves both high detection accuracy and real-time performance for fuel rod weld defect identification. [Methods] A novel defect detection model, named as YOLOv8nWIOU-Fasternet, was designed through comprehensive architectural modifications. Firstly, a distance-aware attention mechanism was integrated into the bounding box regression loss function, incorporating a two-level distance penalty mechanism to focus on ordinary-quality anchor boxes while mitigating adverse gradients from low-quality anchors.Secondly, distribution focal loss(DFL) was incorporated to refine edge-level position estimation, enabling more precise boundary localization through cross-entropy optimization of probability distributions around target labels.Meanwhile, by collecting digital radiography(DR) images of 500 fuel rods, a batch of samples containing abnormal defects such as porosity, gas expansion, incomplete penetration, tungsten inclusion, and blockage were prepared. The area near the end plug weld was selected as the region of interest(ROI), and open-source image annotation tool Labellmg was applied to manual annotation of the defect area to obtain 720 defect images. By systematically expanding the dataset, samples with "pseudo defects" were generated, and a total of 7 200 DR images of fuel rods containing different types of defects were constructed. Among them, the number of different types of defects such as pores, tungsten inclusions, and incomplete penetration was the same in the training set, and were divided into training and validation sets in an 8:2 ratio. An additional 72 fuel rod defect DR images that did not participate in the training and validation process were collected as an independent test set to evaluate and experimentally validate the performance of the defect detection model. Finally, traditional convolutional modules were replaced by a newly designed partial convolution(PConv) structure that selectively applies convolution to a subset of input channels while retaining others, followed by pointwise convolution for spatial information fusion and maintaining representational completeness. [Results] The experimental validation results demonstrate that the proposed YOLOv8n-WIOUFasternet model achieves false negative and false positive rates both below 5%, representing significant performance improvements across multiple metrics and substantial reductions compared to baseline models. The maximum F1score reaches 0.947 at a confidence threshold of 0.285, with corresponding false positive and false negative rates of 3.1% and 3.5%, respectively. The average precision(AP) performance significantly surpasses both traditional feature extraction methods and the original YOLOv8 model across various IoU thresholds. [Conclusions] The proposed model successfully achieves an optimal balance between detection accuracy and computational efficiency through its innovative architectural design. The integration of distance-aware attention mechanisms and partial convolution structures reduces computational overhead while maintaining superior detection performance. This comprehensive approach provides a robust and practical solution for automated fuel rod defect detection in real-world nuclear fuel manufacturing applications, meeting both precision requirements and real-time processing constraints essential for industrial deployment.更多还原