Utilizing Data Analytics in Computer Vision and Robotics for Autonomous Pipeline IntegrityInspections

Abstract

Pipelines play an indispensable role in the secure transmission of fluids, gases, and semi-solid slurries across vast spans. It's imperative to carry out consistent checks for maintaining their integrity to spot issues like corrosion, leaks, or invasive activities that might precipitate calamitous outcomes if not addressed in time. Nonetheless, the manual inspection of such expansive systems presents considerable challenges, being both risky and unreliable. The paper puts forward a analytical data architecture processing for the autonomous execution of pipeline integrity assessments. The robotic crawler utilizes algorithms for sensor fusion to maneuver across challenging landscapes, guided by GPS-RTK tracking with precision to the nearest centimeter. It captures visual information through high- definition cameras and LIDAR, creating geo-tagged three-dimensional representations of the pipeline from several angles. Employing state-of-the-art defect identification processes predicated on convolutional neural networks, this approach facilitates automatic detection of damages, assigning probabilities to each detected issue for a well- informed engineering evaluation. These dynamic models are pivotal in recording changes in the infrastructure's baseline topology with each inspection cycle, aiding in the detection of emerging threat patterns by performing analytics on collected datetime-labelled datasets. Throughout its operation, the detection of anomalies in real-time triggers proactive measures to prevent any further damage, thus averting potential failures. This system strives to revolutionize the way pipeline inspections are conducted, shifting from manual methodologies to an always-on,
autonomous robotic survey. Such advancement ensures a thorough integrity check throughout extensive and dispersed critical energy conveyance networks, markedly mitigating risks and expenses.