X-ray flaw detection is a key link in the detection of internal defects in titanium alloy castings which are used for most important components in aeroengines. However, the existing manual defect detection methods from the X-ray images have common drawbacks such as unstable artificial recognition, misdetection, misjudgment, fails of quantitative analysis, huge workload, and low-quality inspection efficiency. To avoid these drawbacks, this paper proposes a new artificial intelligent (AI) method to detect and recognize the aerospace titanium casting defects from the X-ray images. It includes the target defect positioning method named as filtered selective search algorithm (FSS) and the defect classification method named as evenly distributed convolutional neural network (ED-CNN). In the target positioning step, through statistical analysis of defect characteristics, a filtered selective search algorithm is built with two filters (size and edge curvature). In this way, the FSS algorithm can position the defects with almost 100 % of accuracy, hence avoid missed detection and false detection. In the target classification step, an ED-CNN is constructed with a similar structure of the same number of layers in each feature extraction stage, and its entire architecture is evenly distributed. Compared with other three classic high-performance convolutional neural network models (AlexNet, VGG16 and VGG19), the ED-CNN model has the best performance. The ED-CNN model was tested with 324 targets from 50 original images, a classification accuracy of nearly 90 % was obtained for low density holes, porosity, linear defects, high density inclusions and casting structure. The FSS/ED-CNN method of two phases defect detection proposed in this paper can achieve accurate positioning and high accurate classification of typical defect targets, and is expected to solve the common drawbacks of "manual defect detection". The newly-proposed FSS/ED-CNN method has important research significance and engineering value.

X射线探伤是钛合金铸件内部缺陷检测的关键环节，钛合金铸件用于航空发动机中最重要的部件。然而，现有的基于X射线图像的人工缺陷检测方法具有共同的缺陷，如人工识别不稳定，检测错误，判断失误，定量分析失败，工作量大，检查效率低等。为了避免这些弊端，本文提出了一种新的人工智能（AI）方法，用于从X射线图像中检测和识别航空航天钛铸件缺陷。它包括被称为过滤选择性搜索算法（FSS）的目标缺陷定位方法和被称为均匀分布卷积神经网络（ED-CNN）的缺陷分类方法。在目标定位步骤中，通过对缺陷特征的统计分析，构建了带有两个过滤器（大小和边缘曲率）的过滤选择性搜索算法。这样，FSS算法可以以几乎100％的准确度定位缺陷，从而避免漏检和误检。在目标分类步骤中，在每个特征提取阶段使用具有相同层数的相似结构构建ED-CNN，并且将其整个体系结构均匀地分布。与其他三个经典的高性能卷积神经网络模型（AlexNet，VGG16和VGG19）相比，ED-CNN模型具有最佳性能。使用ED-CNN模型从50个原始图像中对324个目标进行了测试，对于低密度孔，孔隙率，线性缺陷，高密度夹杂物和铸件结构。本文提出的FSS / ED-CNN两阶段缺陷检测方法可以实现典型缺陷目标的准确定位和高精度分类，有望解决“人工缺陷检测”的常见弊端。新提出的FSS / ED-CNN方法具有重要的研究意义和工程价值。