  The Segment Anything Model (SAM) has revolutionized natural image
segmentation, nevertheless, its performance on underwater images is still
restricted. This work presents AquaSAM, the first attempt to extend the success
of SAM on underwater images with the purpose of creating a versatile method for
the segmentation of various underwater targets. To achieve this, we begin by
classifying and extracting various labels automatically in SUIM dataset.
Subsequently, we develop a straightforward fine-tuning method to adapt SAM to
general foreground underwater image segmentation. Through extensive experiments
involving eight segmentation tasks like human divers, we demonstrate that
AquaSAM outperforms the default SAM model especially at hard tasks like coral
reefs. AquaSAM achieves an average Dice Similarity Coefficient (DSC) of 7.13
(%) improvement and an average of 8.27 (%) on mIoU improvement in underwater
segmentation tasks.
