## 3 Discussion  
The comparative evaluation of non-destructive testing (NDT) technologies presented in Table 8 was systematically derived from the literature review conducted in the previous sections. This assessment was based on predefined performance criteria, including defect detection accuracy, consistency, ease of use, acquisition speed, portability, and cost-effectiveness. The data for this comparative analysis were extracted from multiple sources, including empirical studies, experimental evaluations, and industry reports, ensuring a balanced and evidence-based comparison of the technologies. Each parameter in the table weighs one, indicating the presence of a given characteristic for the NDT methods. Including one value for a specific parameter signifies that the technology effectively meets the corresponding performance criterion. This uniform scoring approach ensures objectivity in comparing the strengths and limitations of each method without introducing weighted biases. The most suitable NDT techniques for automated aircraft inspection were identified by analysing these parameters collectively. The results of this comparative analysis highlight that Infrared Thermography (IRT) combined with Photogrammetry emerges as the most suitable NDT technique for the objectives of this research. Figure 17: Dent length and area estimation—colour map and measurements in greyscale [20]. This research has the potential to make significant intellectual contributions to the field of aircraft inspection by advancing automated methods for structural evaluation. An essential contribution is developing a novel inspection system that combines 2D RGB and infrared thermal images with their corresponding 3D models, using photogrammetry to detect surface and near-surface defects in both metallic and composite aircraft materials. This non-contact approach improves defect detection accuracy compared to traditional methods and reduces aircraft downtime and operational costs. Additionally, integrating photogrammetry with infrared thermal and RGB cameras expands the capabilities of non-destructive testing (NDT) techniques, enabling comprehensive 3D reconstructions and more detailed analyses of defect characteristics. To the best of my knowledge, this is the first application of combining advanced cameras with photogrammetry for 3D inspection in the aviation industry. However, similar technology has been successfully used in fields like forensic science, civil infrastructure, and archaeology. Furthermore, many researchers have encountered challenges with unbalanced datasets during machine learning model training due to the absence of open-source datasets containing aircraft defects and the difficulty of accessing aircraft specimens because of intellectual property restrictions and protections enforced by OEMs. Using synthetic images generated from 3D models, combined with real images, offers a valuable solution to these challenges. Finally, integrating all these technologies into a single graphical user interface (GUI) makes the solution user-friendly and accessible to operators without requiring expertise in the field. The findings from this research could have important potential impacts on both the aviation and machine learning industries. In aviation, this approach can offer a more accurate, efficient, and non-invasive method for detecting defects in aircraft materials, which could drastically reduce maintenance costs and aircraft downtime. It can also improve reliability and safety and minimise human error. While this research focuses on smaller-scale aircraft specimens, demonstrating the effectiveness of this technology opens the door to scaling up. Using a UAV system instead of a robotic arm, aircraft maintenance companies can extend these inspection methods to larger aircraft components. For the machine learning industry, an open-source dataset that consists of 2D images and 3D models will be available to other researchers and individuals for further use.