A Deep Learning Framework for Industrial Pipeline Defect Detection using YOLOv8
Industrial pipeline inspection is a critical requirement in sectors such as water distribution, oil transportation, and manufacturing. Conventional inspection approaches remain largely manual, which leads to high operational costs, long inspection durations, and inconsistent defect detection due to human subjectivity. These limitations highlight the need for automated, accurate, and real-time inspection systems capable of reliable defect identification. In this work, we propose a robust real-time defect detection framework based on the YOLOv8 architecture enhanced with Oriented Bounding Boxes (YOLOv8-OBB). The proposed approach is specifically designed to handle elongated and arbitrarily oriented defects, such as cracks and leaks, which are common in industrial pipeline environments. The model is trained and evaluated on a custom dataset comprising 1,224 annotated images distributed across four defect categories: crack, dent, hole, and leak. Extensive experimental results demonstrate strong performance, achieving 83.92% precision, 86.06% recall, and 87.22% mAP@50, while maintaining real-time inference capability with processing times between 5 and 10 milliseconds per image. The results show the proposed system provides an efficient and scalable solution for intelligent industrial inspection and demonstrates strong potential for integration into real-world pipeline monitoring and maintenance platforms.