To ensure safety in aircraft flying, we aimed to use deep learning methods of nondestructive examination with multiple defect detection paradigms for X-ray image detection. The use of the fast region-based convolutional neural network (Fast R-CNN)-driven model was to augment and improve the existing automated non-destructive testing (NDT) diagnosis. Within the context of X-ray screening, limited numbers and insufficient types of X-ray aeronautics engine defect data samples can, thus, pose another problem in the performance accuracy of training models tackling multiple detections. To overcome this issue, we employed a deep learning paradigm of transfer learning tackling both single and multiple detection. Overall, the achieved results obtained more than 90% accuracy based on the aeronautics engine radiographic testing inspection system net (AE-RTISNet) retrained with eight types of defect detection. Caffe structure software was used to perform network tracking detection over multiple Fast R-CNNs. We determined that the AE-RTISNet provided the best results compared with the more traditional multiple Fast R-CNN approaches, which were simple to translate to C++ code and installed in the Jetson^™ TX2 embedded computer. With the use of the lightning memory-mapped database (LMDB) format, all input images were 640 × 480 pixels. The results achieved a 0.9 mean average precision (mAP) on eight types of material defect classifier problems and required approximately 100 microseconds.

中文翻译：

---

AE-RTISNet：具有改进的基于快速区域的卷积神经网络框架的航空发动机射线照相检测系统

为了确保飞机飞行的安全性，我们旨在将具有多种缺陷检测范例的无损检测深度学习方法用于X射线图像检测。使用基于快速区域的卷积神经网络（Fast R-CNN）驱动的模型是为了增强和改进现有的自动化无损检测（NDT）诊断。在X射线检查的背景下，数量有限且类型不足的X射线航空发动机缺陷数据样本可能会导致训练模型应对多种检测的性能准确性出现另一个问题。为了克服这个问题，我们采用了转移学习的深度学习范式来解决单个和多个检测问题。总体，通过对航空发动机射线照相测试检查系统网（AE-RTISNet）进行重新训练并结合八种类型的缺陷检测，可以达到90％以上的精度。Caffe结构软件用于对多个Fast R-CNN进行网络跟踪检测。我们确定，与更传统的多种快速R-CNN方法相比，AE-RTISNet提供了最佳结果，后者更易于转换为C ++代码并安装在Jetson中^™ TX2嵌入式计算机。使用闪电存储映射数据库（LMDB）格式，所有输入图像均为640×480像素。结果对八种类型的材料缺陷分类器问题实现了0.9的平均平均精度（mAP），大约需要100微秒。