## 2 Methods and Materials
### Learning-Based Approaches
[94] (2020) developed a stacked ensemble model for surface defect detection in aircraft visual inspections. This approach combined three CNN architectures--ResNet18, GoogLeNet, and VGG11_bn--as base learners with a logistic regression meta-learner. The ensemble enhanced classification accuracy by leveraging error diversity among different CNN models, achieving 99.8% accuracy and 100% recall in defect detection, outperforming single-model frameworks. Defects examined included aircraft surface cracks, corrosion, scratches, and gouges. Images were sourced from borescope inspections, and a central region of 672 \(\times\) 448 pixels was selected to reduce distortion and lighting issues. Implemented in PyTorch, the model used data augmentation and cross-entropy loss with stochastic gradient descent. This ensemble approach demonstrated improved reliability in automated defect detection, which is crucial for safety-critical systems like aircraft propellers. In 2022, [PERSON] et al. [95] developed a two-step defect recognition and classification process using deep learning to automate aircraft inspections conducted with UAVs. The system used a DenseNet201 CNN model for defect detection and an ensemble of EfficientNetB1, EfficientNetB5, EfficientNetB4, and DenseNet169 CNN models for defect classification. The ensemble approach was designed to handle a range of defect types, including missing paint, dents, lightning strike damage, and patch repairs, achieving up to 100% accuracy in specific classes (e.g., paint damage and dents) and an overall defect detection accuracy of 81.82%. A custom dataset of high-resolution images (4800 \(\times\) 3200 pixels) captured in a maintenance hangar was used, with a semi-automated Python script for selecting regions of interest (ROI) by cropping relevant areas, followed by grayscale conversion to reduce colour dependency. The system was implemented in TensorFlow, utilising transfer learning and data augmentation to mitigate the limitations of a small, imbalanced dataset. Despite these limitations, the approach demonstrated a promising framework for UAV-based defect detection, with high accuracy in detecting and classifying defects critical for aircraft safety. [PERSON] et al. [96] (2022) proposed an automated pixel-level defect detection method for aircraft skin based on an enhanced Mask Scoring R-CNN. The model integrated a Convolutional Block Attention Module (CBAM) and a feature fusion module to refine feature representation alongside a new classifier head with four convolutional layers. An ablation study showed that the classifier head improved the most, boosting segmentation precision by 12.5% and pixel-level accuracy by 16.98%. The system was tested on a custom dataset of 276 images with aircraft skin defects, achieving 27.66% higher bounding box precision and 20.49% better segmentation precision compared to Mask R-CNN. Additionally, the approach demonstrated robustness in handling low-contrast defects, producing smoother and more accurate segmentation boundaries. This method outperformed existing models, showing its effectiveness in precise and reliable defect detection, which is essential for automated aircraft inspection tasks. In 2022, [PERSON] et al. [97] introduced a deep-learning approach to enhance crack detection in turbine blades using infrared induction thermography. Their method cap tured both surface and sub-surface cracks, providing an automated, contact-free solution compared to traditional inspection techniques. The model used ResNet-18 in two configurations: a Large Image Model (LIM) on full-size images and a Small Image Model (SIM) on 64 \(\times\) 64 cropped patches. SIM focused on high-crack-density areas and achieved an F2-score of 0.85, outperforming LIM's 0.60. To address challenges like data imbalance and small crack size, the authors applied focal loss, oversampling, and data augmentation.