基于主动小样本学习的管道焊缝缺陷检测方法  

Active small sample learning based the pipe weld defect detection method

作　　者：刘金海[1] 赵真 付明芮 左逢源 王雷[1] Liu Jinhai;Zhao Zhen;Fu Mingrui;Zuo Fengyuan;Wang Lei(School of Information Science and Engineering,Northeastern University,Shenyang 110004,China;Shenyang Paidelin Technology Company,Shenyang 110000,China)

机构地区：[1]东北大学信息科学与工程学院,沈阳110004 [2]沈阳派得林科技有限责任公司,沈阳110000

出　　处：《仪器仪表学报》2022年第11期252-261,共10页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(U21A20481,61973071);辽宁省兴辽英才项目(XLYC2002046)资助。

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。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.

关 键 词：X射线检测 深度学习 主动小样本学习 价值样本挖掘 

分 类 号：TE88[石油与天然气工程—油气储运工程] TP277[自动化与计算机技术—检测技术与自动化装置] TH878.3[自动化与计算机技术—控制科学与工程]