## 2 Methods and Materials
### Learning-Based Approaches
Implemented in MATLAB, the system demonstrated robustness and adaptability for various defect types, providing a reliable framework for enhanced aircraft maintenance and safety inspections. In 2017, [PERSON] et al. [86] proposed an automated aircraft fuselage defect detection system using Deep Neural Networks (DNNs), specifically CNNs pre-trained on ImageNet (AlexNet and VGG-F). This system combined a VGG-F model as a feature extractor with a linear SVM classifier, achieving high performance in defect detection with 96% accuracy, sensitivity, and specificity. The SURF Interest Point Extractor focused on regions of interest (ROI), resulting in a 6\(\times\) speed-up by only evaluating selected patches. The system processed a 20-megapixel image in 15 s, operating in MATLAB with the MatConvNet library. The dataset consisted of high-resolution RGB images in JPEG format, with data-level balancing through oversampling and undersampling and a binary mask to represent defects. A low-pass Gaussian filter addressed noise in unwashed fuselage images, enhancing defect detection accuracy. This method demonstrated a viable approach to automated, high-accuracy fuselage inspection for aviation maintenance. [PERSON] et al. [91] (2019) developed a hybrid model for aircraft defect classification using a combination of ResNet50 CNN and Prototypical Networks to address the extreme class imbalance in UAV-acquired images. This method was designed for detecting various fuselage defects, such as lightning burns, river rash, screw rash and paint defects. The model utilised data augmentation and few-shot learning to improve classification accuracy, achieving a precision of 0.97 and recall of 0.77 for rare defects. The study found that, depending on the class and sample size, different machine-learning approaches were suitable, leading to a combined model that applies CNNs to common classes and few-shot learning to rare ones. Implemented in Python with high-resolution UAV images, the approach showed that hybrid models effectively balance performance for both common and underrepresented categories. In another similar research project, [PERSON] et al. [33] (2019) presented a UAV-based system for inspecting aircraft exterior screws using advanced computer vision. The UAV employed precise laser-based localisation to position itself relative to the aircraft without GPS, ensuring accurate inspections indoors and outdoors. A CNN with SSD and YOLO models detected screws and identified missing or defective ones. Regions of Interest (ROIs) were selected manually and refined with Generative Adversarial Networks (GANs) and depth maps to enhance pattern matching under various lighting conditions. A 3D model of the aircraft guided the UAV's navigation and camera alignment. Detected screws were matched with expected patterns using a bipartite graph, improving accuracy by referencing a 3D Digital Mock-Up (DMU) of the aircraft. The system achieved over 95% precision and recall, identifying critical defects. For loose screw detection, it analysed screw alignment changes by comparing screw slots with red operator markings. In 2020, [PERSON] et al. [92] explored automated aircraft maintenance using Mask R-CNN for dent detection. Mask R-CNN's instance segmentation capability enabled precise pixel-level detection, crucial for non-uniform dent shapes. The model, trained with data-augmented images and pre-trained COCO weights, achieved an average precision of 54% and recall of 46%. After additional training, precision increased to 69% and recall to 57%. Photos were sourced from the Abu Dhabi Polytechnic hangar and annotated using VGG Image Annotator. Implemented in Python with libraries like TensorFlow and Keras, this study faced limitations, including a small and varied dataset, which impacted model accuracy. Environmental factors, such as lighting and reflections, also affected detection performance, leading to false positives with elements like rivets and raindrops. [PERSON] et al.