# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 5 Conclusions  
This study demonstrated the effectiveness of integrating thermography with machine learning, particularly through the use of synthetic datasets, to improve the detection and characterization of corrosion in steel pipes. Key findings indicate that augmenting experimental data with Finite Element Method (FEM) simulations significantly enhances the performance of machine learning models, as evidenced by the improved metrics of the UNet model in detecting defects.  
The implications of these findings are substantial, suggesting that the approach can overcome common challenges such as data scarcity and overfitting, thereby improving the reliability of defect detection in critical infrastructure. However, this study has limitations, including the dependency on the accuracy of synthetic data and the potential variations in real-world conditions that were not fully replicated in the simulations. Future work should focus on validating these findings with a broader range of materials and conditions, as well as exploring other machine learning architectures and augmentation techniques to further refine defect detection capabilities.  
The contribution of this work to the field is significant, providing a robust framework for enhancing thermographic inspections using advanced data augmentation techniques. This methodology not only addresses current limitations in data availability but also offers practical applications in various industries where early and accurate defect detection is crucial for maintaining safety and performance. By integrating synthetic datasets, this approach sets a precedent for future research and development in non-destructive testing and condition monitoring, ultimately leading to more reliable and efficient maintenance strategies for critical infrastructure.  
Conceptualization, R.K.R., X.M. and C.I.-C.; methodology, R.K.R.; software, R.K.R.; validation, R.K.R.; formal analysis, R.K.R., X.M. and C.I.-C.; investigation, R.K.R., X.M. and C.I.-C.; resources, X.M. and C.I.-C.; data curation, X.M. and C.I.-C.; writing--original draft preparation, R.K.R.; writing--review and editing, R.K.R., X.M. and C.I.-C.; visualization, R.K.R.; supervision, X.M. and C.I.-C.; project administration, X.M.; funding acquisition, X.M. and C.I.-C. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Comparison of UNET+ model performance on different databases.  
**Funding:** This research was supported by the Natural Sciences and Engineering Council of Canada (NSERC), the CREATE oNDuTy Program (funding ref. number 496439-2017), the Canada Research Chair in Multipolar Infrared Vision (MIVIM), Department of Civil Engineering from Universite Laval, and the MITACS Accelerate Program.  
**Data Availability Statement:** All the data for this research are available at this link: [[https://github.com/reza7293/Thermography_CUI_FEM_data_augmentation](https://github.com/reza7293/Thermography_CUI_FEM_data_augmentation)]([https://github.com/reza7293/Thermography_CUI_FEM_data_augmentation](https://github.com/reza7293/Thermography_CUI_FEM_data_augmentation)) (accessed on 1 September 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.