# CTIMS: Automated Defect Detection Framework Using Computed Tomography
## 1 Introduction  
Electrical energy is one of the major pillars of the global economy. It can be generated using different resources such as fossil fuels (coal, natural gas, and petroleum), nuclear energy, and renewable energy sources. For instance, in Canada, the shares of the different power resources are split as follows: hydro at 60%, nuclear at 15%, coal at 7%, gas/oil/others at 11%, and non-hydro renewables at 7% [1]. As Canada is the world's second largest producer of uranium [2] and due to the significant share of nuclear power in the national production, more support is focused on the design as next-generation nuclear energy systems improve its efficiency, such as the CANDU reactor [3]. However, nuclear-based energy is costly due to the high operation cost and extended reactor shutdown durations. These outages are usually caused by periodic maintenance, fault conditions, etc. For instance, the nuclear vault has to stay shut down till all entered objects are manually checked and identified as complete. Therefore, it is important to reduce the maintenance-related outage cost (USD 35 per second USD 3\(M\) per outage) by accelerating the tools' inspection. Different Non-Destructive Testing (NDT) methods have been proposed in the literature to perform tool inspection based on different scanning technologies [4, 5]: thermography imaging, radiography techniques, ultrasonic probes, etc. Computerized Tomography (CT) imaging is one of the emerging NDT technologies that has been used in different applications: quality control [6, 7], quantitative material analysis [8, 9], medicine [10, 11], and geosciences [12]. One of the main challenges in CT-based inspectionis the presence of artifacts and the limited-angle computed tomography, which reduces the CT data quality. Different research works have focused on improving the CT data quality by improving the scanner configuration, such as the newly optimized reconstruction algorithm [13; 14; 15]. The existing CT-based defect inspection methods can be classified into two main categories: image-processing-based techniques and deep learning methods. The first category uses signal and image processing techniques to extract the defect's relevant features or pattern. For instance, this kind of defect inspection is performed using near-net-shape production techniques [16] and the kriging model with statistical models to compute the shape deviation errors [17; 18]. The second category uses deep computer vision models trained on a labeled dataset, taking advantage of the rapid progress of this field [19; 20; 21]. For instance, some approaches use local binary patterns [22], Class-balanced Hierarchical Refinement (CHR) [23], Convolutional Neural Networks (CNNs) [24], and spatial attention bilinear CNNs [25]. However, the performance of the proposed deep learning models depends on the quality and size of the training dataset. At the commercialization level, there exists some X-ray CT-based software for quality control in industrial applications using CT technology such as [26; 27; 28]. In addition, there are some other hardware-software solutions for automated industrial processes, such as the Carl Zeiss AG [29] and VisiConsult X-ray Systems [30]. In this paper, a new CTIMS-Toolbox is proposed for X-ray Computed Tomography (CT) data inspection. It integrates computer vision techniques and state-of-the-art Artificial Intelligence (AI) models for better external/internal tool integrity. In addition, it uses an automation flowchart to perform the auto-inspection using the integrated techniques with less intervention from the user. The proposed hybrid framework focuses mainly on X-ray-based inspection to detect the structural differences between two input X-ray CT volumes or images. The efficiency of the proposed framework is demonstrated using X-ray data of metallic tools used in nuclear power plants. In addition, the proposed CTIMS-Toolbox can be used for any other non-destructive testing or inspection application based on X-ray data.