With the goal of minimizing human-shark conflicts in coastal regions, OWL Integrations is partnering with UConn engineers to develop a device that can accurately detect the presence of a shark. By empowering OWL Integrations' Internet-Of-Things (IOT) DuckLink hardware and ClusterDuck software protocols with Google's Coral Development Board, joint CSE and ECE teams have developed an artificial intelligence (AI) -powered floatation unit capable of real-time autonomous shark detection. Equipped with an onboard camera, the unit captures underwater images and runs a machine learning (ML) model to accurately identify sharks. Upon detection, alerts are transmitted using long range (LoRa) to the OWL's data management system (DMS), enabling timely warnings to beach authorities and the public.

Our team collaborated with Computer Science & Engineering 42 on this project.