# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 2 Literature Review  
The role of thermography in detecting and characterizing subsurface defects is significant, as this method allows for the identification of defects beneath the surface of materials and structures without causing any damage. This is particularly important in the assessment of critical infrastructure where disruptions to operations must be minimized. Recent advancements in thermography have led to the development of state-of-the-art methods providing improved accuracy and sensitivity for subsurface defect detection. Awareness of these new techniques is crucial for assessing the integrity of materials and structures, ensuring the continued safe and effective functioning of these critical assets [5]. Traditional methods for subsurface defect characterization include techniques based on the physical properties of materials [6]. Widely used traditional methods include linear regression, Gaussian regression, support vector machines (SVM) [7], polynomial approximation [8], and classification trees [9]. These methods model the relationship between thermal data features and defect characteristics, providing basic yet effective approaches for defect detection. Traditional machine learning methods are favored for their simplicity, stability, and interpretability. They tend to produce consistent results even with small variations in input data, and their decision-making processes are transparent, making them suitable for applications where accountability is critical [10]. Advancements in thermography have focused on enhancing the detection capabilities and accuracy of defect characterization. These improvements are driven by innovations in both hardware and software, including high-resolution infrared cameras and sophisticated data processing algorithms. Enhanced imaging techniques, such as lock-in thermography and pulsed thermography, have improved the resolution and depth of defect detection. Furthermore, integrating thermography with other NDT techniques like ultrasonic testing and X-ray imaging has resulted in more comprehensive assessments of material integrity. These advancements have expanded the applicability of thermography in various industrial settings, making it a more robust tool for subsurface defect detection [11; 12]. Machine learning methods have revolutionized subsurface defect characterization by automating feature extraction and improving predictive accuracy. These methods can be broadly categorized into traditional machine learning techniques and deep learning approaches. Traditional techniques, as mentioned, offer interpretability and consistent performance with smaller datasets. However, deep learning has achieved state-of-the-art results in tasks such as image segmentation, speech recognition, and sequential data processing. The key advantages of deep learning include its ability to automatically learn relevant features from raw input data, reducing the need for manual feature engineering. Deep learning models are highly scalable and capable of handling large amounts of data, making them ideal for applications that require processing vast datasets. Moreover, these models can learn hierarchical representations of data, capturing complex relationships and patterns within the data. Recent studies have demonstrated the efficacy of deep learning models in thermographic applications, with convolutional neural networks (CNNs) being successfully applied to segment thermal images and identify defect locations with high precision [13]. In recent years, the 1-D approach has been used, which corresponds to training the CNN with thermal temporal signals. This method has shown significant advantages in handling sequential thermal data, improving defect detection accuracy and computational efficiency. The 1-D CNN approach allows for the effective analysis of thermal profiles over time, making it particularly useful for applications where temporal thermal variations are critical [14; 15]. Despite significant advancements, gaps in the current literature need to be addressed.