Methods and Approaches to Automate Pipeline Defect Diagnostics in Real Operational Conditions

The article presents the results of research of methods of pipeline defect diagnosis automation using deep learning models YOLOv11 and Roboflow 3.0. A dataset of 10 000 images was created, the stages of its preparation and model training are described, including the use of data augmentation and hyperparameter optimisation techniques. The results of model testing showed high accuracy of defect detection (YOLOv11: 92%, Roboflow 3.0: 88%). Practical examples of application of the developed methodology for pipeline condition monitoring, including predictive analytics, are considered. The presented approach allows to reduce pipeline maintenance costs, improve safety and minimise human factor.