State of the art applications of deep learning within tracking and detecting marine debris: A survey

arXiv:2403.18067v1 Announce Type: cross
Abstract: 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 …

abstract analysis and analysis applications art arxiv cs.ai cs.cv cs.lg debris deep learning deep learning techniques five litter marine research state state of the art summary survey tracking type up to date