Artificial Intelligence (AI) Powered Detection and Identification of Pressure Vessel External Damage – YOLO-based Application

Abstract Regular visual inspections of pressure vessels by qualified inspectors are vital in the oil and gas industry to ensure vessel integrity, prevent catastrophic failures, and avoid the consequences of misdiagnosis. In the present paper, a deep learning (DL) model is proposed to perform visual inspection of pressure vessels using the You Only Look Once (YOLO) v8 model. Initially, binary classification is developed to diagnose whether the pressure vessel's exterior shell is in excellent condition or includes damage using a training dataset of 5000 real shell surface on-site images from the Abu Madi gas field of the PETROBEL Company, Egypt. An additional model was trained on the same dataset of binary model for multi-class identification of external damages (i.e. corrosion, painting damage, mechanical damage, or brittle fracture). Three models were compared; namely YOLO v5, YOLO v8, and YOLO v9. The highest performance model was YOLO v8. Its detection accuracy reached 93% and 91.5% for binary classification and multi-class classification models, respectively. Implementing this solution in the inspection process will result in a cost reduction since it decreases the need for scaffolding and trained inspectors. This study provides a valuable roadmap for future research on image processing-based pressure vessel damage detection.