# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 3 Experimental Data and Parameters  
### Experimental Data  
#### 3.1.1 Actual Components Dataset  
A key component of the dataset is CT-reconstructed images containing naturally occurring defects, which provide realistic samples for training the model. However, because these defects are embedded within the interior of the components, their exact dimensions are unknown. If defect dimensions are annotated solely based on reconstructed images, the partial volume effect causes blurred boundaries, making it difficult to accurately delineate defect contours and sizes. To obtain more precise annotations, several components were subjected to grinding. Each component was ground in 0.5 mm increments, and the size of defects appearing in each layer was measured. Examples of ground defects and their corresponding measurements are shown in Figure 6. This paper utilizes the open-source software labelme [34] for contour labeling of the defect dataset. Defects in each CT image are labeled by aligning their contours with those in the corresponding grinding image, thereby minimizing inaccuracies caused by blurred reconstruction edges and ensuring labeling accuracy.  
In this study, three common types of defects are considered: hole, loose, and side defects. The first two are naturally formed defects, while the latter is artificially introduced on the component surface. Hole defects are typically small and characterized by a clear grayscale contrast at their center. Loose defects manifest as larger regions composed of multiple small, clustered voids with relatively low central grayscale values. Side defects are distributed along the surface or edges of the component and often partially merge with the contour of the surrounding material in the reconstructed images. Categorizing defects based on these visual characteristics enables the model to learn discriminative features associated with each defect type.  
Figure 7 illustrates two types of images from the training dataset composed of real components. Figure 7a shows reconstructed CT images of real graphite components, along with magnified views of their defect areas. Naturally occurring defects--namely hole and loose defects--can be observed within the interior of the components. In contrast, side defects are manually introduced on the component surface (typically appearing near the image edges). These natural defects exhibit more complex morphology and structural variation, as well as more diverse grayscale patterns, compared to the artificial ones. To facilitate more accurate defect size annotation, the original CT data were reconstructed at a high resolution of 1536 \(\times\) 1536 pixels, with a pixel spacing of 0.5 mm. The resulting  
Figure 6: Grinding image of actual graphite components.  
dataset includes 500 images for training and 100 images for testing. However, due to the limited availability of naturally defective samples, the dataset presents challenges such as small sample size and class imbalance. To address these issues, synthetic data were generated using the Decompound-Synthesize Method (DSM) [35], which performs phantom conversion, background simulation, and defect synthesis to produce CT-like images. DSM is capable of generating realistic artifacts, background noise, and diverse defect geometries, resulting in synthetic images that closely resemble real CT scans. A total of 1500 synthetic images were generated to augment the training set. Figure 6(b) displays representative synthetic images generated using DSM.