College - Author 1
College of Engineering
Department - Author 1
Electrical Engineering Department
Degree Name - Author 1
BS in Electrical Engineering
Date
9-2025
Primary Advisor
Clay McKell, College of Engineering, Electrical Engineering Department
Abstract/Summary
This project develops an AI powered object detection system integrated with OWL DuckLink radios to enable real time remote monitoring. The system benchmarks multiple AI accelerators using a Raspberry Pi 5 to determine their efficiency in low power, long range communication environments. By evaluating the Raspberry Pi 5 CPU (7.62W, 14.5 camera FPS, 3.8 inference FPS), Raspberry Pi AI Camera with Sony IMX500 (6.45W, 30 camera FPS, 9.2 inference FPS), and Raspberry Pi AI Hat with Hailo-8L chipset (7.80W, 30 camera FPS, >30 inference FPS), this project identifies the most effective combination of hardware and software for edge AI applications. The system successfully demonstrates end to end functionality by transmitting object detection data through OWL's LoRa based DuckLink network to the Data Management System, proving feasibility for disaster response, security, and wildlife monitoring applications. These results provide concrete performance metrics and power consumption data that will help OWL Integrations make informed hardware decisions for future AI powered remote sensing deployments, establishing a foundation for scalable real time AI implementations in resource constrained environments.
URL: https://digitalcommons.calpoly.edu/eesp/700