# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 1 Introduction
Convolutional neural networks (CNNs) and their variants have become a key technology in automated defect detection due to robust feature extraction capabilities [14]. Based on algorithmic processes, they are further divided into one-stage and two-stage detection algorithms. One-stage algorithms, such as single-shot multibox detector (SSD) [15] and You Only Look Once (YOLO) [16], predict target categories and positions through linear regression in a single feed-forward network. While offering fast detection speeds, their accuracy may be lower. In contrast, two-stage algorithms, including Regions with CNN features (R-CNN) [17], Faster region-based convolutional neural network (Faster R-CNN) [18], and Mask Region Convolution Neural Network (Mask R-CNN) [19], first generate region proposals and then extract features from each. These algorithms are better suited for high-precision detection tasks but tend to have slower inference speeds compared to one-stage models. Researchers have enhanced one-stage and two-stage algorithms by leveraging their respective strengths, resulting in various target detection methods tailored to specific applications. In the field of defect recognition using CT and radiographic imaging, [PERSON] et al. [20] introduced an enhanced Faster R-CNN framework for pulmonary nodule detection. Evaluated on the LUNA16 dataset, it outperformed YOLOv3 and Cascade R-CNN, with precision increasing from 76.4% to 90.7% and recall from 40.1% to 56.8%. Similarly, [PERSON] et al. [21] developed a system employing ray tracing to generate synthetic radiographic data for training a Mask R-CNN model. Their method achieved over 87% accuracy on a test set of 416 X-ray images of solid propellants. [PERSON] et al. [22] designed an improved Mask R-CNN for multi-class concrete defect detection in bridge structures. The model outperformed the original Mask R-CNN and other deep learning methods by achieving 94.7% accuracy, 95.3% recall, and 90.6% mAP. [PERSON] et al. [23] developed a COVID-19 detection model based on Efficient Net and an attention mechanism for the automated analysis of lung CT images, achieving superior F1 scores (0.9367) compared to the previous year's approaches. Meanwhile, [PERSON] et al. [24] applied YOLOv8 to detect patellar instability on knee MRI, achieving 83% accuracy with significantly faster inference than a junior radiologist. Despite these advancements, existing methodologies face persistent challenges in reliably detecting small-sized defects under complex, high-noise CT imaging environments, underscoring the need for enhanced detection algorithms. DL methods for small target detection are widely applied across various fields. For instance, [PERSON] et al. [25] developed a multi-scale region-generating network (MRPN) using an enhanced Faster R-CNN model to improve detection accuracy in spark plug defect identification, achieving 89% accuracy and 97% recall on a custom X-ray image dataset. [PERSON] et al. [26] integrated an improved Mask R-CNN model with the Sobel edge detection algorithm to extract region-of-interest features for textile surface defect detection, achieving an accuracy of 97.8% and outperforming other models by up to 6.45%. To address low detection accuracy in traditional forging inspection methods, [PERSON] et al. [27] designed an Efficient Net-based model enhanced with a feature pyramid network and attention mechanisms, achieving a 95.69% mAP and an F1 score of 0.94 on fluorescent magnetic particle inspection images. [PERSON] et al. [28] addressed weld defect detection with an improved YOLO model that integrates a reinforced multi-scale feature module and an efficient feature extraction module, resulting in a mAP of 92.9% and an inference speed of 61.5 FPS, making it suitable for real-time industrial deployment. [PERSON] et al.