Advanced Pipe Identification: A Deep Learning Approach in Petroleum Engineering

This study created YOLOv۱۲, a novel object detection framework applied to petroleum engineering for automated pipe recognition and classification from industrial datasets. Leveraging YOLOv۱۲'s robust real-time processing performance, the research focused on amplifying an automated and trustworthy approach in pipeline and asset management. The developed general model had a mean Average Precision (mAP) of ۹۸.۷۰%, with Average Precision scores across all IoU ranges (۰.۵-۰.۹۵) indicating precision at ۹۹.۸%, and recall at ۹۹.۶%, illustrating a very high accuracy and robustness level incorporating a very imbalanced data set. Model training stabilized between ۲۰ and ۳۰ epochs, suggesting good efficiency with terminal convergence without overfitting. Focal loss provided good effectiveness in classes; meanwhile, data augmentation allowed for a more generalizable model. Performance-wise, the model offers practical and efficient solutions for scalable and verifiable automated inventory management, lowering operational costs. In particular, the challenges of class imbalance, and model generalization were addressed to begin the foundation development for future petroleum engineering research on specific applications. The application of YOLOv۱۲ in petroleum engineering is an impactful and innovative application of deep learning in a niche industrial context.

کلیدواژه ها:

Pipe Identification ، Deep Learning ، Object Detection ، Petroleum Engineering