  Garbage and waste disposal is one of the biggest challenges currently faced
by mankind. Proper waste disposal and recycling is a must in any sustainable
community, and in many coastal areas there is significant water pollution in
the form of floating or submerged garbage. This is called marine debris.
Submerged marine debris threatens marine life, and for shallow coastal areas,
it can also threaten fishing vessels [I\~niguez et al. 2016, Renewable and
Sustainable Energy Reviews]. Submerged marine debris typically stays in the
environment for a long time (20+ years), and consists of materials that can be
recycled, such as metals, plastics, glass, etc. Many of these items should not
be disposed in water bodies as this has a negative effect in the environment
and human health. This thesis performs a comprehensive evaluation on the use of
DNNs for the problem of marine debris detection in FLS images, as well as
related problems such as image classification, matching, and detection
proposals. We do this in a dataset of 2069 FLS images that we captured with an
ARIS Explorer 3000 sensor on marine debris objects lying in the floor of a
small water tank. The objects we used to produce this dataset contain typical
household marine debris and distractor marine objects (tires, hooks, valves,
etc), divided in 10 classes plus a background class. Our results show that for
the evaluated tasks, DNNs are a superior technique than the corresponding state
of the art. There are large gains particularly for the matching and detection
proposal tasks. We also study the effect of sample complexity and object size
in many tasks, which is valuable information for practitioners. We expect that
our results will advance the objective of using Autonomous Underwater Vehicles
to automatically survey, detect and collect marine debris from underwater
environments.
