Below-the-Canopy Computer Vision for Drone-Based Search and Rescue

Drones have the potential to scale up search and rescue operations to find people who are lost in the wilderness. Reliably detecting people in densely forested, under-canopy environments remains challenging. This project focuses on advancing person detection in the real-world ForestPersons dataset (comprising over 96,000 under-canopy images) to enable autonomous search and rescue missions using commercial drone systems.

Intern: Joshua Chen

Mentor: Aurora Schmidt (AOS/QNN)