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Up-to-Date Scoping Review of Object Detection Methods for Macro Marine Debris
Summary
This review examines over two decades of research into object detection methods for identifying macro marine debris, with a focus on deep learning advances made in the past five years. Following PRISMA-ScR guidelines, the authors assessed current techniques and identified key progress and remaining gaps in automated marine litter identification systems.
Being able to accurately identify litter in a marine environment is crucial to cleaning up our seas and oceans. Research into object detection techniques to support this identification has been underway for over two decades. However, there have been substantial advancements in the past five years due to the implementation of deep learning techniques. Following the PRISMA-ScR guidelines, we provide an in-depth summary and analysis of recent and significant research contributions to the object detection of macro marine debris. From cross-referencing the results of the literature review, we deduce that there is currently no benchmarked framework for evaluating and comparing computer vision techniques for marine environments. Subsequently, we use the results from our analysis to provide a suggested checklist for future researchers in this field. Furthermore, many of the respected researchers in this field have advocated for a comprehensive database of underwater debris to support research developments in intelligent object detection and identification.
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