摘要
为实现精密焊件内部缺陷的X射线微弱信号视觉检测,研究在强噪声、大灰度梯度背景下微弱图像信息的分割方法。首先提出一种基于扫描线的自适应梯度阈值方法来快速确定可疑缺陷区域,以减少数据处理量、降低分割难度;然后提出基于反几何扩散模型的焊缝缺陷X射线微弱信号提取算法,利用在反几何扩散过程中形成的一系列自适应阈值面,根据改进的分类规则对缺陷微弱图像信息进行标记分割,同时识别噪声。实验表明,该方法能在亮度不均匀、边缘模糊、强噪声的X射线图像中准确提取出焊缝缺陷微弱信号,失真小且效率高。
To achieve visual detection of small signal in X-ray defect-image of precision weldment, the segmentation method of weal image information with strong noise and large gray gradient background was studied. An adaptive gradient-threshold method based on the scan line was proposed to quickly identify suspicious defect-region, which significantly reduced the amount of data and the segmentation difficulty. The algorithm to extract small signal in X- ray image of weld defects was developed based on the anti-geometric diffusion model. Through a series of adaptive threshold surfaces-formed by anti-geometric diffusion process, the weal image information of defects was marked and classified, and the noise was identified according to the improved classification rules. The experimental results indi- cated that the method could accurately extract weal signal in X-ray defect-image with uneven brightness, low con- trast and strong noise. The distortion was low and the efficiency was high.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2013年第10期2557-2561,共5页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(51205265)
重庆市自然科学基金资助项目(cstc2011jjA70005)
重庆市基础与前沿研究计划资助项目(cstc2013jcyjA70009)~~
关键词
X射线图像
焊缝
缺陷检测
反几何扩散
微弱信号
X-ray image
welding seam
defect detection
anti-geometric diffusion
weal signal
