# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 4 Experimental Evaluation and Analysis
### Test Results of Actual Components Dataset
For loose defects, detection is relatively easier due to their larger size and distinctive texture, so most models perform well, but Defect R-CNN still leads with 0.98. Side defects, characterized by clean edges along component boundaries, are generally easier to detect, which explains the uniformly high AP-bbox-50-side values (\(\geq\)0.949) across most models, whereas earlier two-stage detectors like Faster R-CNN (0.426) and Mask R-CNN (0.526) struggle to match this performance. In terms of segmentation performance, Defect R-CNN achieves the highest mAP-segm of 0.956, outperforming Mask R-CNN (0.297) by 221.9%, and also exceeding YOLOv11 (0.381) and Efficient Net (0.411) by large margins. Again, for category-specific AP-segm-50, Defect R-CNN achieves top scores of 0.9 (hole), 0.98 (loose), and 0.988 (side)--showcasing its comprehensive segmentation capability across defect types. Regarding inference speed, Defect R-CNN achieves 76.2 FPS, outperforming traditional two-stage frameworks such as Mask R-CNN (53.5 FPS) and Faster R-CNN (68.4 FPS). This efficiency gain results from the merging of multi-branch EPCB modules into a single-branch structure during inference, which simplifies computation without sacrificing accuracy. Although one-stage models like YOLOv11 (91.1 FPS), Efficient Net (100.6 FPS), and RT-DETR (111.8 FPS) operate faster, they often trade speed for precision--especially in detecting small or subtle defects like holes. Defect R-CNN offers a well-balanced solution, delivering high precision while maintaining real-time inference speed suitable for industrial applications. Figure 11 displays a reconstructed CT image from the test set, containing three annotated defect types--side, loose, and hole. Figure 12 provides zoomed-in detection results from six different models on the same image, enabling direct visual comparison. Confidence scores are shown alongside each predicted bounding box to illustrate detection certainty. In Figure 12a,b, both Faster R-CNN and Mask R-CNN detect only a portion of the side and loose defects, with confidence scores generally below 60 (side: \(\sim\)30-56, loose: \(\sim\)45-55). Notably, both models fail to detect the hole defect entirely, reflecting their limited capability in capturing small defects. This is consistent with their poor AP-bbox-50-hole \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Faster R-CNN** & **Mask R-CNN** & **Efficient Net** & **RT-DETR** & **YOLOv11** & **Defect R-CNN** \\ \hline mAP-bbox & 0.373 & 0.415 & 0.664 & 0.776 & 0.655 & 0.983 \\ AP-bbox-50-hole & 0.048 & 0.05 & 0.442 & 0.383 & 0.187 & 0.98 \\ AP-bbox-50-loose & 0.645 & 0.67 & 0.574 & 0.953 & 0.829 & 0.98 \\ AP-bbox-50-side & 0.426 & 0.526 & 0.977 & 0.993 & 0.949 & 0.988 \\ mAP-segm & — & 0.297 & 0.411 & — & 0.381 & 0.956 \\ AP-segm-50-hole & — & 0.05 & 0.0544 & — & 0.0456 & 0.9 \\ AP-segm-50-loose & — & 0.604 & 0.675 & — & 0.663 & 0.98 \\ AP-segm-50-side & — & 0.237 & 0.503 & — & 0.433 & 0.988 \\ FPS & 68.4 & 53.5 & 100.6 & 111.8 & 91.1 & 76.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection results of different models. values (0.048 and 0.05, respectively) and low mAP-bbox scores reported in Table 3. In Figure 12c, Efficient Net identifies all side defects, but with varying confidence scores, and detects the loose and hole defects with low confidence (30.6 and 34.1, respectively). This suggests a lack of robustness to grayscale fluctuations and partial edge blending, especially for low-contrast defects. RT-DETR, shown in Figure 12d, demonstrates excellent performance in detecting side and loose defects, achieving high confidence values (side: \(\geq\)94, loose: 84.8). It attains the highest AP-bbox-50-side (0.993) among all models, affirming its strength in structured region recognition. However, it detects the hole defect with relatively low confidence (30.5), consistent with its lower AP-bbox-50-hole (0.383), revealing difficulty in handling fine-grained features.