## 2 Main Approach
### Detection on Description  
The AE-RTISNet inspection of X-ray images was required to pre-train the detection of defect types and provide the location or region of the defects. The AE-RTISNet algorithm improved the accuracy used in the X-ray image defect recognition non-destructive radiation testing at the aerial engine maintenance factory. AE-RTISNet was based on the Fast R-CNN algorithm model and developed for detection, localization, and classification. Many different deep neural network models, including DetectNet [16], Fast R-CNN [17], and YOLO [18], have been proposed for object detection and semantic segmentation. In this paper, a model based on the aeronautics engine radiographic testing inspection system was used to determine the bounding box of object defect inspection in X-ray images. The prepared transfer learning model architecture was evolved from the Fast R-CNN model and Fully Convolutional Network (FCN) frameworks for object detection and semantic segmentation. The layers of Fast R-CNN learn the attention mechanism object detection with a Region Proposal Network (RPN) [19]. The model of Fast R-CNN is, thus, sufficiently flexible and robust to be applicable. The Fast R-CNN model was used to provide the bounding-box object detection for the design of convolutional networks. The normal Fast R-CNN was extended to allow marking of the defect feature maps using pooling layers, which could lead to faster convolution speeds and better accuracy [20]. Under the JetsonTMX2 embedded computer, the AE-RTISNet compiler for C++ code was used to model the eight types of object classification feature maps and a normal regression was used to estimate the object bounding boxes compiler for the deep learning neural network for object detection [16]. This is an extension to the AE-RTISNet to increase the number of hidden layers. Some changes to accommodate multi-class object detection were also made. This is different from the common practice when applying FCNs [21]. The determination of the structure model for multi-class object detection  
Figure 4: Detection of eight types of defects. contains training and validation steps. Figure 5 illustrates the training and validation procedures in which the L1/L2 loss function is adopted so that the results are not biased to a single class. This paper proposes a deep Fast R-CNN based framework function, which can not only efficiently detect defect damage from X-ray image files but can also be easily adjusted to other aerial part material damage detection problems. We used the Fast R-CNN algorithm model by applying a deep learning algorithm structure, where pre-training the AE-RTISNet model provided the ability to output both the defect regions and the eight types of damages from the input X-ray image datasets. Additionally, the transfer learning method was introduced to reduce the required amount of data and yield high accuracy. Recently, certain well-known SCI papers demonstrated neural nets that modelled their design on deep learning-based object damage recognition methods. For example, researchers proposed deep layer R-CNN to detect different kinds of cracks while evaluating more than five R-CNN model architectures for the special detection of material corrosion from the input images [22; 23]. The Fast R-CNN training algorithm made-up for the disadvantages of R-CNN and SPPnet [17] while improving on their speed and accuracy. Thus, the Fast R-CNN algorithm method was shown to work with large input datasets that are first cropped into small images of fixed size, and with CNNs applied to classify whether cracks or corrosion are contained in each small image with a fixed size. Thus, the AE-RTISNet model detection gained a more perfect mAP than the R-CNN and SPPnet model. Using the AE-RTISNet model accelerated Fast R-CNN by 10\(\times\) at test time. The AE-RTISNet model training time was also reduced by 3\(\times\) due to the faster proposal feature extraction.