This project investigates the transformation of a standard miniature aerial platform into an intelligent, perception-driven system capable of autonomously detecting and localizing a person-of-interest using real-time object detection. Rather than relying on biometric identity verification, the framework employs YOLO-based computer vision models to detect target individuals in dynamic aerial environments. A baseline pretrained YOLO model is first evaluated for general person detection, followed by a fine-tuned YOLO model trained on task-specific imagery to improve detection robustness under varied lighting, pose, and environmental conditions.
The system integrates real-time visual inference with flight control, forming a closed-loop perception-action architecture in which detection confidence informs navigation adjustments. This research evaluates the feasibility of deploying object detection models on resource-constrained edge hardware while maintaining real-time responsiveness. Beyond technical validation, the study discusses operational boundaries and ethical considerations associated with perception-driven autonomous flight in controlled environments.
Watch the team present this project at 56:20 in the session recording here.
Keywords: facial recognition, autonomous drones, computer vision, deep learning, edge AI, human–drone interaction, identity recognition, biometric systems, real-time perception, ethical AI, autonomous navigation
Faculty Advisor
Steve Barry serves as technology and data leader at a global investment bank. He brings more than 25 years of leadership experience in the realms of technology and finance. Furthermore, Steve’s expertise extends to data science and AI, with a demonstrated proficiency in platforms like AWS, Azure, and GCP.
Steve is currently a Lecturer in the Master’s in Applied Data Science at the University of Chicago. He graduated with a master’s degree from the same program in 2020. His background in finance and technology, has been pivotal in applying data science in practical contexts.
Complementing his professional experience, Steve served as a Teaching Assistant in the same University of Chicago Master’s program for three years for multiple courses including: BDP, DEPA, ML, and Data Visualization. This role allowed him to bridge the gap between academic knowledge and its application in the finance and tech industries. Steve is an enthusiastic lifelong learner that provides him unique perspective to communicate complex concepts clearly.