# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 4 Experimental Evaluation and Analysis  
### Test Results of Actual Components Dataset  
After training, the optimal parameter configuration of the model is saved and tested on the actual components test dataset. Figure 10 shows the PR curves of the six evaluated models across three defect classes: hole, loose, and side, as well as their overall performance. Compared to all other models, Defect R-CNN consistently exhibits the best performance, with smooth PR curves forming a high-arching shape that indicates superior detection capability across all defect categories. In particular, the PR curve for hole defects, which are the smallest and most challenging to detect, remains relatively stable for Defect R-CNN even at higher recall values--demonstrating the model's effectiveness in capturing fine-grained features. By contrast, both Faster R-CNN and Mask R-CNN show sharp precision drops when recall exceeds 0.2, especially for hole defects, suggesting that they struggle to generalize well on smaller or low-contrast targets. The lack of specialized edge-aware features and deeper multi-scale representation likely contributes to their limited performance. Efficient Net performs well for side defects, maintaining relatively high precision  
Figure 9: Loss curves of six models. across most recall levels. However, its performance on hole and loose defects degrades significantly as recall increases, with noticeable fluctuations in the PR curve. This may stem from the model's reliance on global image features, which are less effective in capturing localized, small-scale anomalies. YOLOv11 also shows competent performance on the side and loose defects, but the PR curve for hole defects reveals severe instability--precision falls rapidly as recall increases beyond 0.2. This suggests that YOLO-based detectors, while fast, may be prone to over-detection and false positives when applied to small or ambiguous regions. RT-DETR, as a transformer-based detector, performs better than traditional CNN backbones in handling loose and side defects. However, the curve for hole defects is jagged and lacks smooth progression, indicating unstable confidence in predictions across thresholds. This may be due to RT-DETR's default settings for anchor-free decoding, which could be suboptimal for tiny object detection in CT images. Overall, the smoothness and area under the curve of Defect R-CNN across all classes validate the effectiveness of the architectural improvements introduced in this work, which enhance fine defect localization and mitigate false positives in high-noise CT environments. To further quantify each model's performance, we calculated the mAP, category-specific AP, and FPS for both detection and segmentation tasks. These metrics provide a comprehensive evaluation of model performance across varying thresholds of the PR curve. Table 3 summarizes the performance of the proposed Defect R-CNN and five benchmark models across these metrics, as defined in Section 3.2. Figure 10: PR curves of six models. Defect R-CNN consistently outperforms all baseline models across both detection and segmentation tasks. It achieves the highest mAP-bbox of 0.983, surpassing the next best model, RT-DETR (0.776), by 26.7%, and significantly exceeding traditional models like Mask R-CNN (0.415) and Faster R-CNN (0.373). On category-specific metrics, Defect R-CNN demonstrates superior precision for all three defect types, scoring above 0.98 in AP-bbox-50 across hole, loose, and side defects. This performance advantage is especially pronounced for hole defects, which are the smallest and most challenging to detect due to their blurry edges and weak grayscale contrast. Most models, including YOLOv11 and Efficient Net, show significant drops in AP for this class, with Efficient Net achieving only 0.442. In contrast, Defect R-CNN reaches 0.98, a 121.7% improvement, thanks to its edge-aware EPCB module, which enhances fine-grained feature extraction.