Detecting abyssal megabenthos in the Great Atlantic Sargassum Belt – Comparing quantitative assessments via manually processed OFOS-videos with AUV-images utilising deep learning models

Abstract As the deep sea alone covers over 50 % of the Earth's surface and represents the largest benthic system, the assessment of deep-sea megafauna communities is of great importance. However, even today the number of studies investigating this system is small and is further hampered by the resource- and time-consuming nature of traditional manual surveys. Therefore, it is of great importance to find efficient sampling methods and optimize processing techniques. On the one hand, sampling can be done by a variety of non-invasive methods, such as video documentation using seabed observation systems (OFOS) or seabed mapping using autonomous underwater vehicles (AUV). On the other hand, automated data analysis could be a faster alternative to manual processing by incorporating deep learning models. Here, we compare both sampling and processing techniques by providing novel and unique data on epi-megafauna from two overlapping abyssal plains at 10°N in the Great Atlantic Sargassum Belt. The 2017 datasets provided by OFOS were processed manually, while a manually trained deep learning detection model based on the YOLO V5 algorithm was used to process the 2014/15 AUV dataset. Megafauna composition, total density and higher taxonomic densities as well as the occurrence of Sargassum patches were calculated and compared between the datasets. Significantly higher densities with additional megafauna forms were observed when the datasets were collected by an AUV and processed by the model. The results were discussed in terms of the advantages and disadvantages of the two approaches to sampling and processing.