# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 1 Introduction  
Since the 1960s, the High-Temperature Gas-Cooled Reactor (HTGR) has undergone multiple development stages. As a representative of Generation IV nuclear reactor technology, it offers high thermal efficiency, enhanced safety, and versatile applications [1]. The safe and reliable operation of HTGR is pivotal for advancing sustainable nuclear energy deployment and aligning with global environmental protection objectives. Central to HTGR architecture are nuclear graphite components, which serve dual roles as both core structural materials and neutron reflectors [2]. Characterized by low thermal neutron absorption cross-sections and exceptional high-temperature stability, these components are critical for neutron flux moderation, thermal insulation, and maintaining geometric integrity during prolonged operation [3]. Since these components are not replaceable after reactor startup,their longevity plays a crucial role in determining the overall operational lifespan of the reactor system [4]. However, inherent defects introduced during manufacturing, transportation, or in-service irradiation can progressively degrade their mechanical properties, potentially leading to irreversible structural damage and compromising the safety margin of the reactor system [5]. Consequently, the development of precise and reliable defect detection methodologies for nuclear graphite components is indispensable to ensuring the structural integrity and long-term operational safety of HTGRs. The Institute of Nuclear and New Energy Technology at Tsinghua University has developed a multi-row helical computed tomography (CT) for full-volume inspection of nuclear graphite components in the HTGR [6]. A 600,000 kw high-temperature reactor comprises over 20,000 components, each ranging in size from 600 mm to 2000 mm. Volume defects are randomly distributed within the components, most measuring between 1 and 2 mm [7]. Due to the large volume of components and the subtle nature of defects, manual inspection is impractical. Therefore, an automated detection algorithm with high accuracy and computational efficiency is crucial for CT inspection tasks. In response to this need, researchers have developed a variety of automatic detection algorithms, which can be broadly categorized into machine vision (MV) and deep learning (DL) approaches [8]. Traditional MV-based detection begins with image acquisition of defective objects, followed by the application of handcrafted image processing algorithms tailored to specific defect characteristics. The extracted features, typically related to texture or shape, are then manually analyzed to classify defect types. [PERSON] et al. [9] proposed an MV method for bearing surface defect detection. They introduced an Lc-MNN image segmentation algorithm and an SCV feature selection algorithm. With 600 test samples, it achieved a 99.5% identification rate. To automate surface defect inspection of ultrasound probes in a production line, [PERSON] et al. [10] developed a machine vision system integrated, achieving a detection accuracy of 98.63% and a classification accuracy of 81.90%. [PERSON] et al. [11] achieved accuracies of 100% (training set) and 96.15% (validation set) in detecting adhesive defects in solid engine linings by incorporating considerations for device deflection and cameras compatibility. [PERSON] [12] developed a POL method targeting fiber optic connector end-face defects, achieving at least 97.14% accuracy, approximately 20% improvement in accuracy, and 6-7 times higher efficiency than traditional manual inspection. Nevertheless, these MV methods rely on manually engineered features, extensive domain expertise, and customized algorithms, resulting in high labor and time investments. With the rapid development of high-performance computing and hardware, deep learning--by extracting features that are difficult to define in traditional algorithms--offers a more efficient solution, positioning it as the dominant method in defect detection [13].