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.

翻译：分割一切模型（SAM）革新了自然图像分割技术，但其在水下图像上的表现仍然受限。本文提出AquaSAM，这是首次尝试将SAM的成功扩展至水下图像，旨在创建一种适用于多种水下目标分割的通用方法。为此，我们首先在SUIM数据集中自动分类并提取各类标签。随后，我们开发了一种简单的微调方法，使SAM能够适应通用水下前景图像分割任务。通过涵盖人类潜水员等八类分割任务的大量实验，我们证明AquaSAM在珊瑚礁等困难任务上的表现显著优于原始SAM模型。在水下分割任务中，AquaSAM的平均Dice相似系数（DSC）提升了7.13%，平均交并比（mIoU）提升了8.27%。