Work

Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection

Year: 2025

Type: article

Abstract: Defect detection in industrial computed tomography (CT) images remains challenging due to small defect sizes, low contrast, and noise interference. To address these issues, we propose Defect R-CNN, a novel detection framework designed to capture the structural characteristics of defects in CT images. The model incorporates an edge-prior convolutional block (EPCB) that guides to focus on extracting edge information, particularly along defect boundaries, improving both localization and classification. Additionally, we introduce a custom backbone, edge-prior net (EP-Net), to capture features across multiple spatial scales, enhancing the recognition of subtle and complex defect patterns. During inference, the multi-branch structure is consolidated into a single-branch equivalent to accelerate detection without compromising accuracy. Experiments conducted on a CT dataset of nuclear graphite components from a high-temperature gas-cooled reactor (HTGR) demonstrate that Defect R-CNN achieves average precision (AP) exceeding 0.9 for all defect types. Moreover, the model attains mean average precision (mAP) scores of 0.983 for bounding boxes (mAP-bbox) and 0.956 for segmentation masks (mAP-segm), surpassing established methods such as Faster R-CNN, Mask R-CNN, Efficient Net, RT-DETR, and YOLOv11. The inference speed reaches 76.2 frames per second (FPS), representing an optimal balance between accuracy and efficiency. This study demonstrates that Defect R-CNN offers a robust and reliable approach for industrial scenarios that require high-precision and real-time defect detection.

Source: Applied Sciences

DOI: 10.3390/app15094825