# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 4 Experimental Evaluation and Analysis
### Test Results of Test Components Dataset  
Due to the limited size of the actual component test set and the small number of defect instances, it is expected that the resulting evaluation metrics may be higher or lower than typical cases. To provide a more comprehensive assessment of the model's performance, we further evaluated their generalization ability using the test component dataset. Since the test components in the evaluation set contain only hole defects, performance metrics are computed exclusively for this defect category. The calculated AP-bbox-50-hole values for Faster R-CNN, Mask R-CNN, Efficient Net, RT-DETR, YOLOv11, and Defect R-CNN are 0.025, 0.03, 0.388, 0.343, 0.083, and 0.99, respectively. These results demonstrate that when applied to completely unseen data, the Defect R-CNN model continues to exhibit strong detection performance, while the accuracy of other models declines noticeably. As illustrated in Figure 13, this disparity becomes evident in the visualized detection results.  
Figure 12: Comparative detection results of six models in a CT image.  
Figure 13: _Cont._Figure 13: _Cont_.  
Figure 13a presents CT-reconstructed images of the test components, composed of four rectangular aperture plates with hole sizes of 4 mm, 2 mm, 1 mm, and 3 mm. As observed in the image, many of the 1 mm holes are barely visible due to their extremely small volume under the current CT system resolution and are only faintly discernible at a few locations.  
Figure 13: Detection results of different models on the test component.  
In the evaluation using this test set, Faster R-CNN, Mask R-CNN, and YOLOv11 perform poorly, with AP-bbox-50-hole values of 0.025, 0.03, and 0.083, respectively. As seen in Figure 13b,c,f, all three models fail to detect the hole defects entirely and instead misclassify them as loose or side defects--constituting false positives. This reflects significant issues in sensitivity when faced with subtle defects. Figure 13d,e shows the test results of Efficient Net and RT-DETR, which show relatively better performance. However, both models detect only a limited number of true hole defects, and frequently mislabel defects as side or loose, especially near the image edges and center. This suggests insufficient feature learning, particularly in handling complex grayscale artifacts and distinguishing subtle variations in defect morphology. In contrast, Defect R-CNN outperforms the other models in detection accuracy and confidence. As illustrated in Figure 13g, it successfully detects nearly all hole defects with high confidence scores, including those of smaller size and in challenging regions. Except for one missed detection in a 2 mm aperture, the model achieves near-complete coverage, highlighting its robustness in multi-scale defect detection and generalization capability on unseen data. However, it should be noted that ring artifacts present in the reconstructed CT images--bearing texture similarities to actual defects--can still lead to occasional false positives, even for Defect R-CNN. This emphasizes the need for further refinement through combined hardware--software optimization to suppress artifact interference and improve overall defect recognition reliability.  
In summary, Defect R-CNN outperforms other models in both detection accuracy and confidence. It excels in managing artifacts and complex backgrounds, making it more suitable for industrial inspection tasks requiring high accuracy and robustness. While RT-DETR and Efficient Net offer faster detection speeds, they face challenges in detecting small-scale and low-contrast defects in more intricate scenes.