小径管X射线焊缝图像缺陷识别算法
Recognition algorithm of small-diameter tube X-ray weld-ing defect image
肖扬 1高炜欣 2邓国浩3
作者信息
- 1. 西安石油大学电子工程学院,西安, 710065
- 2. 西安石油大学电子工程学院,西安, 710065;西安石油大学陕西省油气井测控技术重点实验室,西安, 710065
- 3. 西安石油大学陕西省油气井测控技术重点实验室,西安, 710065
- 折叠
摘要
针对小径管X射线焊缝图像缺陷检测精确率低的现状,通过对图像进行特征分析并结合稀疏字典学习,提出一种基于图像分割的小径管焊缝图像缺陷检测算法.首先,对小径管焊缝图像进行两步图像分割获得感兴趣区域;其次,提取焊缝缺陷,得到缺陷疑似局部图像;最后,提出以不同类型原子间相关性最小为目标的小径管焊缝缺陷字典矩阵数学模型并使用K-SVD算法进行求解,利用该字典矩阵实现圆形缺陷、线形缺陷和噪声的分类鉴别.为提高系统实时性,使用并行编程对图像分割算法进行加速.结果表明,改进后缺陷字典矩阵对圆形缺陷识别成功率为0.974,线形缺陷识别成功率为0.967,且具有较快的识别速度,实现了小径管焊缝图像缺陷的有效识别.
Abstract
To address the current situation of low accuracy rate of small-diameter tube welding image defect detection,by combining image feature analysis and sparse dictionary learn-ing,a small-diameter tube welding defect detection algorithm based on image segmentation is proposed.Firstly,using two-step image segmentation way acquires the region of interest which is in small-diameter tube welding image.Secondly,the suspected defect region is obtained by extracting welding de-fect.Finally,we propose a mathematical model of the diction-ary matrix of small-diameter tube welding defects with the ob-jective of minimizing correlations between different types of atoms and solve it by using K-SVD algorithm.After that,the dictionary matrix is used to classify circular defects,strip de-fects and noise.To improve the real-time performance of the system,we use parallel programming to accelerate the image segmentation algorithm.The results show that the recognition rate of the proposed method is 0.974 for circular defects and 0.967 for strip defects,and the recognition speed is fast,which enables the effective recognition of defects in small-diameter tube welding image.
关键词
小径管/焊缝缺陷/图像分割/稀疏字典学习Key words
small-diameter tube/welding defect/image segmentation/sparse dictionary learning引用本文复制引用
基金项目
陕西省自然科学基金(2020JQ-788)
陕西省重点研发项目(2020GY-179)
陕西省重点研发计划项目(2024GX-YBXM-003)
出版年
2024
NETL
NSTL
万方数据