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State of the art applications of deep learning within tracking and detecting marine debris: A survey
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This survey analyzed 28 recent deep learning studies applied to marine debris detection and tracking, finding that YOLO-based models significantly outperform other object detection methods. Testing on a small curated dataset revealed low accuracy and high false positive rates, underscoring the urgent need for a comprehensive annotated database of underwater plastic debris.
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.
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Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.