基于多幅X射线数字图像的缺陷自动识别技术

在现有的X射线数字图像自动识别方法中,多采用对单幅数字图像进行孤立评判的方法。由于此类方法中阈值选取难以最优化,因而存在一定的误判率。为了解决这一问题,提出了一种X射线数字图像自动识别新方法。该方法将识别过程分为两步:缺陷提取和缺陷跟踪。第一步利用传统方法在每幅图像中分离出潜在缺陷。这一步保证真缺陷能全部提取出来,而不考虑伪缺陷的数量。第二步力图找出同一试件不同图像中分离出的缺陷之间的相互关系。如果第一步某一图像中分离出的某一缺陷在其他图像中都找不到相对应的缺陷区域,就定义该缺陷为伪缺陷,也就是说,真缺陷在不同图像中必须满足一定的几何关系。多幅图像中的缺陷跟踪综合利用了极线约束、三维重建和三线性约束等立体视觉算法。该方法的检测效果已经利用航空发动机叶片X射线数字图像得到验证。试验结果表明:利用该方法可以提高真缺陷的识别率,降低误判率。

AUTOMATED DEFECTS RECOGNITION TECHNIQUE BASED ON MULTIPLE RADIOGRAPHIC IMAGES

The current automated recognition of digital radio-graphic images is mostly carried out in individual images. Certain false detections exist because the threshold values of these methods are difficult to be optimized. To solve this problem, a new automated recognition methodology for digital radio-graphic images is put forward, which is based on a two-step analysis: Defects extraction and defects tracking. The first step segments potential defects in each radioscopic image using a classic method. In this step the identification of real defects is ensured while the number of false detections is not considered. The second step attempts to find a correspondence between the segmented potential flaws from image to image. The key idea of this work is to consider false detections as those potential defects, which can't be corresponded with any other one in the multiple images. The defects tracking of potential defects in the images follows the principles of multiple view geometry, that is the position of real flaws in the radioscopic images must fulfill some geometric constraints. The inspection throughput of the method has been verified on real radioscopic images recorded from turbine blade. Using this method the real defects can be detected with high probability and the false detections can be eliminated.