# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 3 Challenges of Subsurface Defect Characterization by Thermography
### Feature Extraction  
Beyond architecture and parameter optimization, effective feature extraction is crucial for thermography applications. Techniques like Extended Blind End-Member and Abundance Extraction (EBEAE) [30] and Convolutional Autoencoders (CAEs) [31] have been instrumental in improving feature extraction. EBEAE enhances the identification and characterization of thermal features, leading to more accurate defect depth estimation in composite materials. [PERSON] and [PERSON]'s work exemplifies EBEAEs' effectiveness in extracting precise features from thermal maps for SVM classification [32]. CAEs, particularly in denoising and data enhancement roles, maintain deep spatial information while reducing noise, as seen in [PERSON] et al.'s improved DCAE (Data-enhanced Convolutional Autoencoders) model [33; 34].  
In addition to these methods, features can also be extracted using specific algorithms associated with different thermography techniques. Pulsed thermography features can be obtained by performing algorithms such as Thermal Signal Reconstruction (TSR) [35], Pulse Phase Thermography (PPT) [36], and Principal Component Analysis (PCA) [37]. These methods analyze the thermal response over time to identify and characterize subsurface defects. Lock-in thermography, on the other hand, allows for feature extraction by analyzing amplitude and phase maps obtained through lock-in (LI) algorithms. These maps provide detailed information about the thermal properties and the presence of defects within the material [38]. Incorporating these approaches enhances the capability to detect and quantify subsurface defects more accurately. By integrating a variety of feature extraction techniques, both traditional and advanced, we can improve the reliability and accuracy of thermographic inspections, thereby providing a more comprehensive assessment of material integrity.  
Addressing the challenge of dataset size is paramount in machine learning, especially for thermography, where data diversity and volume directly influence model performance. Larger datasets enable training of more complex models, enhancing performance and reducing overfitting risks [39; 40]. Innovative strategies include deep autoencoders, which leverage unsupervised learning to process and analyze thermographic data, identifying subtle, nonlinear patterns in noisy images. [PERSON] et al.'s work with deep autoencoders facilitates detailed visualization of defect features and accurate identification in composite materials [41].  
Semi-supervised learning, which utilizes both labeled and unlabeled data, enhances model performance and generalization, as demonstrated by [PERSON] et al.'s semi-supervised framework for transient thermography [42]. Generative Adversarial Networks (GANs) address data scarcity by generating synthetic data that mimics real thermal images, augmenting existing datasets and improving model robustness [43]. [PERSON] et al.'s application of GANs for thermal image augmentation highlights their potential in enhancing thermographic analysis [44], showing a flowchart suggested by the generative network for increasing the size of the dataset.