Real-Time Pipeline Tracking System on a RISC-V Embedded System Platform

• Published in: 2024 IEEE 14th Symposium on Computer Applications & Industrial Electronics (ISCAIE) pp. 227 - 232
• Main Authors: Wei, Eric Sia Siew, Aromoye, Ibrahim Akinjobi, Hiung, Lo Hai
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.05.2024
• Subjects: Autonomous aerial vehicles; Computational modeling; Drones; Embedded systems; Hardware; Inspection; Pervasive computing; Pipeline inspection; Pipelines; Real-time systems; Reliability; RISC; Three-dimensional displays; YOLO
• ISSN: 2836-4317
• DOI: 10.1109/ISCAIE61308.2024.10576323

Pipeline infrastructures are the most suitable means of transporting oil and gas products, making these infrastructures demand reliable inspection methods to ensure their integrity and reliability. Current inspection techniques are labour-intensive, error-prone, safety-threatening, time-consuming, and limited coverage. This paper presents a real-time Pipeline Tracking System hosted on the RISC-V Embedded System Platform, aiming to automate the inspection process. The model was trained using the YOLOv7 algorithm, which is trained to detect and track pipelines and is deployed on the VisionFive 2 Single Board Computer, a RISC-V embedded system platform which offers capabilities in 3D image processing, making it an ideal platform for automated pipeline inspection in resource-constrained environments. The system is designed for integration with unmanned aerial vehicles (UAVs), providing an onboard computer for vision-based detection. Experimental results demonstrate compatibility in resource-constrained environments, emphasising computational efficiency and tracking accuracy. This work contributes to automating pipeline inspection processes, enhancing safety, and advancing RISC-V technology. Future work includes optimising computer vision performance and hardware implementation on a drone.