Undergraduate Research: An Archive - 2022 Program

THESIS TITLE
Deploying Deep
Learning to Estimate
the Abundance of
Marine Debris From
Video Footage
ADVISER
Constantinos
Hadjistassou,
Associate Professor of
Engineering, University
of Nicosia
Cathy Teng ’22
COMPUTER SCIENCE
Certificate in Environmental Studies
The ubiquity of plastic goods in modern society
has led to the omnipresence of synthetic
materials in the marine environment. To address
the problem of plastic pollution, I developed
an image classifier based on the YOLOv5 deep
learning tool that can classify and localize
plastic debris and marine life in images and
video recordings. The image classifier, when
augmented by the region-of-interest line and
centroid-tracking counting methods, was able
to count plastic debris and fish displayed in
video footage. The centroid tracking method
achieved a counting accuracy of 79% and
proved more efficient due to its ability to track
the geometric centers of the bounding boxes
of detected objects. Additionally, the proposed
classifier achieved a mean average precision
of 89.4% when validated for nine categories of
objects. This method’s impact could be enhanced
substantially if it is integrated into other
surveying methods or applications.
CLIMATE AND
ENVIRONMENTAL SCIENCE
8