Power Line Inspection
Based on Computer Vision Models Gridguard
Power Line Inspection
Based on Computer Vision Models Gridguard
Embedded Files
Based on Computer Vision Models Gridguard
This project focused on designing a real-time, automated inspection system for power line insulators using drones, computer vision, and deep learning. Traditional manual inspections are costly, time-consuming, and pose safety risks, while this solution leverages aerial imagery to improve efficiency and accuracy. The system integrates a multi-stage inference pipeline with object detection, segmentation, and classification, enabling fault detection under varied environmental conditions. The outcome is a deployable system capable of identifying insulators and detecting faults with high accuracy and real-time performance, significantly reducing reliance on manual inspections.
Meet the Team
Meet the Team
Anas Kunda
Electrical and Computer Engineering – Applied AI
Samden Lepcha
Electrical and Computer Engineering – Applied AI
Mohammad Mahdi Asgari
Computer Science
Stanley Vianney Foumi Nkwengwa
Rehoboth Mubedi
Kevin Xia
Team Power Line Detection using CV Models in Design Day
The Client and Technical Advisor
The Client and Technical Advisor
Miodrag Bolic
Associate Professor
University of Ottawa
Role in Project：Client for the Power Line Inspection project. Professor Bolic recognized expert in embedded
systems, and biomedical signal processing. Provided research directions and critical feedback on system design.
Iraj Mantegh
Technology and Team Lead
Technology and Team Lead- AI Aerial Robotics & Counter-UAS at the National Research Council (NRC)
Role in Project：Technical Advisor for the Power Line Inspection project. With expertise in AI systems, and autonomous platforms, Dr Mantegh guided implementation strategies and troubleshooting.
Project Gallery
Project Gallery
Project Poster
Final Prorotype Demo
Final Prorotype Demo
202507221815.mp4Page updated
Google Sites
Report abuse