## 2 Methods and Materials
### Learning-Based Approaches  
Machine learning is essential in modernising aircraft maintenance by making defect detection faster and more accurate. By examining patterns in images, these methods improve inspection speed and reliability, helping reduce human mistakes and increase safety. Machine learning techniques have also been successfully applied in various fields beyond aviation, demonstrating their adaptability and effectiveness in complex inspection tasks. A notable example is their application in cultural heritage preservation, as highlighted in a recent study [84] on the automated detection of rising dampness in historical mansonries. This research integrated deep learning with Infrared Thermography (IRT) to identify moisture-related deterioration in historical structures, such as the Holy Aedicule of the Holy Sepulchre and the Msma'a historical building. Using a combination of the PSPNet image segmentation model with a ResNet-50 backbone, the study achieved high accuracy (0.93) and Intersection over Union (IoU) (0.89) despite working with a relatively small dataset. The results underscored the potential of AI-driven models in enhancing the precision of non-destructive testing while ensuring cost-effective and non-intrusive monitoring. The successful implementation of machine learning in this field further reinforces its transformative role in improving inspection processes across different industries, including aviation. Table 5 provides a snapshot of various machine-learning approaches used in aircraft inspection. Each row presents a specific study and details key components: feature extraction, classifier, ROI selection, data processing, performance metrics, type of defect, environment and libraries, and dataset. The feature extraction section covers techniques like CNNs, which help highlight important image details related to defects. Classifier choices include models like SVMs and neural networks for classifying and detecting faults. ROI selection shows how methods target defect-prone areas to focus computational power where it matters most. Data processing improves image clarity and balances datasets, while performance metrics like accuracy and recall show how effective each approach is. The type of defect points out the specific issues each study aims to detect, from cracks to corrosion. Environment and libraries list the software and tools that make these methods possible, like MATLAB or TensorFlow. Finally, dataset details describe the data used, including image size, resolution, and balance. In the following sections, we will examine how these studies enhance aircraft inspection, discussing strengths, challenges, and their impact on maintenance. \begin{table}
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[PERSON] et al. [85] (2016) proposed a multi-source sensor approach for aircraft skin crack inspection using a Support Vector Machine (SVM) classifier optimised with a Genetic Algorithm (GA). The system integrated image and ultrasonic sensors to capture surface and subsurface crack features. Image features, such as angular second moment, contrast, correlation, and entropy, were extracted using the Gray Level Co-occurrence Matrix (GLCM), while ultrasonic features included echo height and echo time, offering depth and internal structure information. A mobile inspection platform with suction cups enabled stable, precise data collection. The GA optimised the SVM by selecting optimal parameters (penalty factor C and kernel parameter \(\sigma\)), improving accuracy to 93.3%, compared to 88.9% with SVM alone and 71.5% with single sensor methods. This dual-sensor fusion approach improved crack detection accuracy, overcoming challenges of low resolution and human error in traditional visual inspections.