Abstract:
Marine pollution is a significant issue in Sri Lanka,
with the country being a major contributor to marine debris.
Marine pollution has the potential to adversely impact marine
and coastal biodiversity, as well as the fishing and tourism
industries. Current methods for monitoring marine debris involve
labor-intensive approaches, such as visual surveys conducted
from boats or aircraft, beach clean-ups, and underwater transects
by divers. However, an emerging trend in many countries is the
use of Unmanned Aerial Vehicle (UAV) imagery for monitoring
marine debris due to its advantages, including reduced labour requirements,
higher spatial resolution, and cost-effectiveness. The
work presented in this study utilizes multispectral UAV imagery
to monitor marine debris in a coastal area of Ambalangoda, Sri
Lanka. For the automated detection of marine debris in captured
images, this work replicates the state-of-the-art CutPaste method
for region detection and utilized the ResNet-18 model with Faster
R-CNN for the final classification of marine debris instances. The
implemented approach demonstrated a classification accuracy of
approximately 60% in automatic marine debris detection, laying
the groundwork for potential enhancements in the future.
