State of the art applications of deep learning within tracking and
  detecting marine debris: A survey
Zoe Moorton Dr. Zeyneb Kurt Dr. Wai Lok Woo
Deep learning techniques have been explored within the marine litter problem
for approximately 20 years but the majority of the research has developed
rapidly in the last five years. We provide an in-depth, up to date, summary and
analysis of 28 of the most recent and significant contributions of deep
learning in marine debris. From cross referencing the research paper results,
the YOLO family significantly outperforms all other methods of object detection
but there are many respected contributions to this field that have
categorically agreed that a comprehensive database of underwater debris is not
currently available for machine learning. Using a small dataset curated and
labelled by us, we tested YOLOv5 on a binary classification task and found the
accuracy was low and the rate of false positives was high; highlighting the
importance of a comprehensive database. We conclude this survey with over 40
future research recommendations and open challenges.