DOI: https://doi.org/10.15368/theses.2020.176
Available at: https://digitalcommons.calpoly.edu/theses/2370
Date of Award
12-2020
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
Franz Kurfess
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
Recent years have seen several projects across the globe using drones to detect sharks, including several high profile projects around alerting beach authorities to keep people safe. However, so far many of these attempts have used cloud-based machine learning solutions for the detection component, which complicates setup and limits their use geographically to areas with internet connection. An on-device (or on-controller) shark detector would offer greater freedom for researchers searching for and tracking sharks in the field, but such a detector would need to operate under reduced resource constraints. To this end we look at SSD MobileNet, a popular object detection architecture that targets edge devices by sacrificing some accuracy. We look at the results of SSD MobileNet in detecting sharks from a data set of aerial images created by a collaboration between Cal Poly and CSU Long Beach’s Shark Lab. We conclude that SSD MobileNet does suffer from some accuracy issues with smaller objects in particular, and we note the importance of customized anchor box configuration.