# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 6 Discussion and Conclusions  
In this study, we proposed Defect R-CNN, a deep learning model specifically tailored for defect detection in CT images. The model incorporates shape-prior knowledge and introduces the EPCB, which adaptively enhances edge feature extraction during training. This significantly improves the model's sensitivity to small and low-contrast defect regions. Based on EPCB, we further designed the EP-Net backbone, which employs a multi-branch structure for robust feature learning during training and is structurally compressed into \begin{table}
\begin{tabular}{c c c c c} \hline  & **Mask R-CNN** & **+EPCB** & **+EP-Net** & **Defect R-CNN** \\ \hline mAP-bbox & 0.592 & 0.774 & 0.985 & 0.986 \\ AP-bbox-50-hole & 0.05 & 0.397 & 0.98 & 0.98 \\ AP-bbox-50-loose & 0.651 & 0.982 & 0.989 & 0.989 \\ AP-bbox-50-side & 0.538 & 0.987 & 0.988 & 0.989 \\ mAP-segm & 0.313 & 0.787 & 0.961 & 0.979 \\ AP-segm-50-hole & 0.05 & 0.397 & 0.913 & 0.969 \\ AP-segm-50-loose & 0.642 & 0.978 & 0.98 & 0.98 \\ AP-segm-50-side & 0.249 & 0.987 & 0.989 & 0.989 \\ FPS & 51.91 & 50.97 & 50.96 & 78.85 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of ablation experiments. Figure 14: Comparison of detection results between Mask R-CNN and Defect R-CNN. a single-branch configuration during inference. This design achieves an effective balance between detection accuracy and computational efficiency. We trained Defect R-CNN alongside five state-of-the-art models--Faster R-CNN, Mask R-CNN, Efficient Net, RT-DETR, and YOLOv11--under identical conditions. Testing on the actual component dataset showed that Defect R-CNN outperforms these models in feature extraction and hole defect localization, achieving a mAP-bbox of 0.983 and a mAP-segm of 0.956, significantly improving upon the baselines and reflecting enhanced spatial precision. Notably, the model also demonstrated efficient inference, achieving 76.2 frames per second, thereby offering both accuracy and speed. In addition, evaluation of the test component dataset--comprising multiple instances of small-sized hole defects--demonstrated the model's robustness. Defect R-CNN achieved an AP-bbox-50-hole of 0.99, detecting nearly all defects with high confidence, even in regions where feature contrast was low or artifacts were present. To further evaluate the effectiveness of each architectural enhancement, ablation experiments were conducted on the same dataset. Replacing the ResNet50 bottleneck with EPCB increased mAP-bbox from 0.592 to 0.774, while the integration of the EP-Net backbone further improved AP-bbox-50-hole to 0.98. Merging the multi-branch structure into a single-branch design boosted inference speed from 50.96 to 78.85 FPS, with mAP-bbox and mAP-segm still reaching 0.986 and 0.979, respectively. These results confirm that each improvement contributes to both accuracy and efficiency, reinforcing the model's suitability for industrial CT defect detection. Although the model is evaluated on CT images of HTGR graphite components, its design and performance characteristics suggest strong potential for broader application in other industrial defect detection applications. Looking ahead, further improvements can be achieved by enhancing CT image quality. This could include the use of higher-resolution detectors during image acquisition or the application of image enhancement techniques such as edge sharpening to increase contrast between defect boundaries and surrounding material. Such efforts would facilitate more precise feature extraction and contribute to even greater detection accuracy, especially in complex industrial CT scenarios. Conceptualization, Z.J., J.F., T.Z., R.L. and Y.S. ; Methodology, Z.J., J.F., T.Z., R.L. and Y.S. ; Software, T.Z., R.L. and Y.S. ; Validation, Z.J., J.F., T.Z. and R.L. ; Formal analysis, Z.J., J.F., T.Z. and R.L. ; Investigation, J.F. and T.Z. ; Resources, P.C., J.M. and Y.S. ; Data curation, Z.J., J.F., R.L., P.C. and J.M.