Comparison of Corrosion Segmentation Techniques on Oil and Gas Offshore Critical Assets
Issued Date
2023-01-01
Resource Type
Scopus ID
2-s2.0-85164965544
Journal Title
2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023
Rights Holder(s)
SCOPUS
Bibliographic Citation
2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023 (2023)
Suggested Citation
Sookpong S., Phimsiri S., Tosawadi T., Choppradit P., Suttichaya V., Utintu C., Thamwiwatthana E. Comparison of Corrosion Segmentation Techniques on Oil and Gas Offshore Critical Assets. 2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023 (2023). doi:10.1109/ECTI-CON58255.2023.10153134 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/88070
Title
Comparison of Corrosion Segmentation Techniques on Oil and Gas Offshore Critical Assets
Author's Affiliation
Other Contributor(s)
Abstract
Corrosion is one of the biggest problems that can lead to fatal disasters in the industry. Investigate corrosion and perform timely maintenance on the asset to prevent corrosion issues. However, the investigation of the inspector onsite can lead to a time-consuming and dangerous problem. For that reason, corrosion detection from offshore asset images is necessary. This paper proposes the implementation of a segmentation technique for automatically detecting corrosion damage on oil and gas offshore critical assets. We compare three semantic segmentation architectures, namely UNET, PSPNet, and vision transformer. The image data was collected by unmanned aerial vehicles (UAV). The experiment also compared the full-image dataset and sliced-image dataset with 512 × 512 pixels of the image. The results are calculated using the F1 score and IoU score of the predicted and annotated mask. The experiment shows that ViT-Adapter trained with a full-image dataset receives the best IoU score and F1 score, which are 0.8964 and 0.9451, respectively. However, the specialist inspector prefers the result from the slicing experiment since the slicing prediction offers a more precise corrosion mask.