This study reviews advanced methods for corrosion detection and characterization in pipes using thermography, with a focus on addressing the limitations posed by small datasets. Thermography captures temperature distributions on the surface of pipes to identify subsurface defects. The challenges of sequential data processing, neural network performance, feature extraction, and dataset size are discussed, with proposed solutions such as advanced algorithms, feature selection techniques, and data augmentation. Given the significant gap in the current literature, there is a need for larger, more diverse datasets to train more robust and accurate machine learning models. A case study combining experimental data with Finite Element Method (FEM) simulations demonstrates that augmenting datasets with synthetic data significantly improves defect detection accuracy. These findings highlight the potential of integrating thermography with machine learning to enhance defect detection, providing insights for future research and practical applications.

Defects in in-service pipelines, including corrosion under insulation (CUI) and thickness loss, pose significant challenges to asset integrity in the oil and gas industry. These defects are particularly hazardous as they often remain unnoticed. The automation of defect detection processes can assist inspectors in reducing analysis time, costs, and human error. However, recent attempts to adopt machine learning for automated defect detection from thermal images have been hindered by limited data availability. This paper presents a novel approach to address this issue by utilizing thermal data augmentation, generating synthetic sub-surface defects via finite element modeling. The resulting synthetic thermal images, combined with real images, are then used to train a deep learning model for the automatic detection of potential defects. Additionally, this study explores the efficacy of synthetic thermal images in enhancing the generalization of the detection model.

It is crucial to accurately detect moisture-induced defects in steel pipe insulation in order to combat corrosion under insulation (CUI). This study enhances the capabilities of infrared thermography (IRT) by integrating it with top-performing machine learning models renowned for their effectiveness in image segmentation tasks. A novel methodology was developed to enrich machine learning training, incorporating synthetic datasets generated via finite element method (FEM) simulations with experimental data. The performance of four advanced models—UNet, UNet++, DeepLabV3+, and FPN—was evaluated. These models demonstrated significant enhancements in defect detection capabilities, with notable improvements observed in FPN, which exhibited a mean intersection over union (IoU) increase from 0.78 to 0.94, a reduction in loss from 0.19 to 0.06, and an F1 score increase from 0.92 to 0.96 when trained on hybrid datasets compared to those trained solely on real data. The results highlight the benefits of integrating synthetic and experimental data, effectively overcoming the challenges of limited dataset sizes, and significantly improving the models’ accuracy and generalization capabilities in identifying defects. This approach marks a significant advancement in industrial maintenance and inspection, offering a precise, reliable, and scalable solution to managing the risks associated with CUI.

This study investigates the semantic segmentation of common concrete defects when using different imaging modalities. One pre-trained Convolutional Neural Network (CNN) model was trained via transfer learning and tested to detect concrete defect indications, such as cracks, spalling, and internal voids. The model’s performance was compared using datasets of visible, thermal, and fused images. The data were collected from four different concrete structures and built using four infrared cameras that have different sensitivities and resolutions, with imaging campaigns conducted during autumn, summer, and winter periods. Although specific defects can be detected in monomodal images, the results demonstrate that a larger number of defect classes can be accurately detected using multimodal fused images with the same viewpoint and resolution of the single-sensor image.

The thermographic inspection of buildings is a powerful and non-invasive method for monitoring and diagnosing building performance and structural integrity. It can effectively detect moisture, evaluate heat loss, and assess building’s roofs. The early detection of problems in a building allows owners to fix issues before they become more severe and costly. One of the challenges in automating thermal analysis for building inspections comes in the form of distinguishing between different surfaces. This paper presents an automated process pipeline using coupled thermal and visible images for building inspections to assist inspectors in adapting strategies and methods to discriminate the thermal signatures between different kinds of surfaces. A deep learning method is employed to segment visible images texturally. Thermal images are then analyzed based on the resulting segmentation. Moreover, a multi-modal dataset is introduced, presenting coupled thermal and visible images acquired during multiple building inspections.