基于SUSAN算法的X射线焊缝图像缺陷提取  

Defects extraction of X-ray weld image based on SUSAN

作　　者：陈强[1] 荀一[1] 崔笛[2] 鲍官军[1] 杨庆华[1] 

机构地区：[1]浙江工业大学特种装备制造与先进加工技术教育部/浙江省重点实验室,浙江杭州310032 [2]浙江大学生物系统工程与食品科学学院,浙江杭州310027

出　　处：《机电工程》2012年第10期1159-1162,共4页Journal of Mechanical & Electrical Engineering

基　　金：浙江省特种装备制造与先进加工技术重点实验室开放基金资助项目(2011EM002)

摘　　要：针对X射线焊缝图像存在对比度不高、灰度分布不均衡、图像中噪声多以及动态模糊等特点导致焊缝缺陷难以提取等问题,提出了一种基于SUSAN算法的焊缝缺陷提取方法。首先设定了采集图像的有效焊缝区域,减小了所要处理的数据量,进而对该区域进行了中值滤波处理;然后通过SUSAN算法找到了每一行焊缝缺陷区域的入口点及出口点,从而实施了缺陷分割;接着再结合数学形态学运算,滤除了孤立的噪声点与间断点,实现了缺陷区域准确定位。在VC++6.0平台上,针对常见的几类缺陷,包括裂纹、气孔、烧穿、未熔合等,取共100张焊缝缺陷图片进行了算法测试,与人工观测结果相对,正确率约为87%。研究结果表明,焊缝图像噪声过多以及对比度太小是未能正确分割出焊缝缺陷的主要原因。Aiming that X-ray image of weld contains low contrast, uneven gray distribution, much image noise and more dynamic fuzzy and other shortcomings which could lead to difficult to extract the weld defects, an algorithm of weld defects extraction was put forward based on small univalue segment assimilating nucleus（SUSAN）. Firstly, the region of interested（ROI） area was extracted to reduce the amount of processed data and median filter of this area was done, then the entry point and exit point of weld defects of each line were found by SUSAN algorithm. By combing mathematical morphological operation, the noise of isolated spot and discontinuous points was filtered to extract the accurate position of defect area. Through the VC＋＋ 6.0 platform for experiments to test some common defects,the correct segmentation rate is about 87% compared with artificial observation. The results show that the contrast is too low and the noise is too much of weld defects are the main reasons that could not segment the weld defects correctly.

关 键 词：X射线焊缝图像 有效焊缝区域 焊缝缺陷提取 SUSAN 数学形态学 

分 类 号：TG115.28[金属学及工艺—物理冶金] TG44[金属学及工艺—金属学]