Segmentation of Weld Defects Based on Water-Flooding Principle

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Segmentation of Weld Defects Based on Water-Flooding Principle

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- Segmentation of Weld Defects Based on Water-Flooding Principle
- Vol. 44, Issue 3, Pages: 90-94(2010)
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN911.7
- Online First：10 March 2010，
  
  Published：2010
- 稿件说明：
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Segmentation of Weld Defects Based on Water-Flooding Principle[J]. 2010, 44(3): 90-94.
DOI：

Segmentation of Weld Defects Based on Water-Flooding Principle[J]. 2010, 44(3): 90-94. DOI：

摘要

针对焊缝射线检测图像缺陷分割速度慢、精度低的问题

提出了一种基于水淹没原理的缺陷分割算法

即依据水流借势取径、盈满则溢的思想

对缺陷及周边区域进行逐级淹没

并根据淹没面积变化来确定分割阈值.该算法首先利用灰度曲线波形分析检测缺陷

获取缺陷种子点

然后从种子点出发对图像进行逐级淹没

淹没过程采用逐线搜索策略.淹没结束后

通过分析各水位淹没面积相对增量的变化来确定阈值水位.实验结果表明

水淹没分割算法的分割耗时大约是Alaknanda方法的5.3%

Kaftandjian方法的25%

分割误差大约是Alaknanda方法的27.33%

Kaftandjian方法的53.14%

因此具有较好的分割速度和精度.

Abstract

To improve the rate and accuracy of weld defect segmentation in radiographic images

a segmentation method based on water-flooding principle is proposed. According to the principle water flows downwards to the low-lying-land and runs over when it is full

the region of a defect and its neighbor are flooded and the threshold value is determined in terms of the growth of the flooded area. The waveform analysis of gray level profiles is used to detect a defect and get the seed point. Then

a flooding process is carried out starting from the seed point

where a line by line search strategy is employed. Once flooding

the threshold is determined by analyzing the relative increment of the flooded area. The experimental results show that the computation cost of the proposed method is 5.3% and 25% of that of Alaknanda method and Kaftandjian method

and the segmentation error gets 27.33% and 53.14%.

关键词

Keywords

references

WANG G, LIAO T W. Automatic identification of different types of welding defects in radiographic images [J]. NDT & E International, 2002,35(8): 519-528.

LIAO T W, LI Y. An automated radiographic ndt system for weld inspection: part ii, flaw detection [J]. NDT E International, 1998,31(3): 183-192.

KAFTANDJIAN V, DUPUIS O, BABOT D, et al. Uncertainty modelling using Dempster-Shafer theory for improving detection of weld defects [J]. Pattern Recognition Letters, 2003,24(1/3): 547-564.

冈萨雷斯. 数字图像处理 [M].阮秋琦,译. 第2版. 北京: 电子工业出版社, 2003: 192-193.

RAMBABU C, CHAKRABARTI I. An efficient immersion-based watershed transform method and its prototype architecture [J]. Journal of Systems Architecture, 2007,53(4): 210-226.

KAFTANDJIAN V, DUPUIS O, BABOT D, et al. Uncertainty modelling using Dempster-Shafer theory for improving detection of weld defects [J]. Pattern Recognition Letters, 2003,24(1/3): 547-564.

ALAKNAND A, ANAND R S, KUMAR P. Flaw detection in radiographic weldment images using morphological watershed segmentation technique [J]. NDT E International, 2009,42(1): 2-8.

喻罡, 李鹏, 缪亚林,等. 多分辨超声心动图像分割模型 [J]. 西安交通大学学报, 2006,40(4): 432-436.

YU Gang, LI Peng, MIAO Yaling, et al. Multi-resolution segmentation model for echocardiographic image [J]. Journal of Xi'an Jiaotong University,2006,40(4): 432-436.

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