GRID HAWK presents a modular UAV-based inspection system that can be mounted on drones with different airframe shapes and sizes. It is designed to enhance the safety, efficiency, and accuracy of power line inspections. By integrating a lightweight payload with high-resolution visible and infrared cameras, along with GPS localization and onboard edge AI processing, GRID HAWK is capable of capturing and analyzing data in real time. The system leverages advanced computer vision algorithms to detect potential defects, reducing the need for risky manual inspections while providing valuable insights to operators.

This project demonstrates the feasibility of combining modular sensor integration and machine learning for practical infrastructure monitoring. GRID HAWK’s design emphasizes scalability and adaptability, enabling future enhancements such as expanded datasets, two-way command communication, and optimized flight endurance. Overall, the project highlights the potential of UAVs and edge AI to transform traditional inspection practices into a safer, faster, and more efficient workflow.