【摘要】 基于X射线探伤的焊缝缺陷检测是维护管道安全的关键环节，实现高精度、高效率的缺陷智能检测是推动无损检测智能化、现代化的重要方面。目前，基于深度学习的缺陷检测方法很难达到较高的精度和效率，因其需要大量标注样本且难以获取。针对这一问题，提出了一种基于主动小样本学习的管道焊缝缺陷检测方法。首先，基于轻量级神经网络提取小样本特征，以数据驱动的方式训练缺陷检测器；然后，推理无标签样本计算检测及分类不确定度并充分挖掘价值样本；最后，根据高价值样本微调网络参数，以最小的成本获得较高的性能提升。实验结果表明，方法能够利用更少的样本，在保证运行效率的前提下，提高约8%的精度。更多还原

【Abstract】 The detection of weld defects based on the X-ray flaw detection is a key part of maintaining pipeline safety. The realization of high-precision and high-efficiency intelligent defect detection is an important aspect to promote the intelligence and modernization of nondestructive testing. At present, it is difficult to achieve high accuracy and efficiency with deep learning-based defect detection methods because they require a large number of labeled samples and are difficult to obtain. To address this problem, this article proposes an active small sample learning-based defect detection method for pipe welds. First, the defect detector is trained in a data-driven manner by extracting small sample features based on a lightweight neural network. Then, the inference of the unlabeled samples is used to calculate the detection and classification uncertainty, which could fully exploit the value samples. Finally, the network parameters are fine-tuned according to the high-value samples to obtain a high performance improvement with minimal cost. Experimental results show that the proposed method can improve the accuracy by about 8% with fewer samples and the guaranteed operational efficiency.更多还原