Automated Computer Vision Inspection of Physical Pipelines

Automated fault identification through computer vision is transforming pipeline monitoring and maintenance by enabling continuous, real-time inspection that improves efficiency, safety, and regulatory compliance. The technology uses models like YOLOv8 to detect defects such as cracks, corrosion, and leaks in pipelines transporting petroleum, gas, water, and other substances, thereby reducing the risk of environmental disasters and expensive repairs. The system architecture comprises low-cost, low-power camera nodes placed along pipelines that stream video to a Raspberry Pi gateway, where the data is processed and analyzed using machine learning models. The results are then sent to an IoT server for further analysis and reporting. The project utilizes the "Storm drain" dataset from Roboflow to train the model, which is then deployed for inferencing to identify pipeline issues. This approach not only enhances worker safety by identifying potential hazards but also supports predictive maintenance and decision-making through the analysis of data trends over time.