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.