Segment Anything Model (SAM) has revolutionized the way of segmentation. However, SAM's performance may decline when applied to tasks involving domains that differ from natural images. Nonetheless, by employing fine-tuning techniques, SAM exhibits promising capabilities in specific domains, such as medicine and planetary science. Notably, there is a lack of research on the application of SAM to sonar imaging. In this paper, we aim to address this gap by conducting a comprehensive investigation of SAM's performance on sonar images. Specifically, we evaluate SAM using various settings on sonar images. Additionally, we fine-tune SAM using effective methods both with prompts and for semantic segmentation, thereby expanding its applicability to tasks requiring automated segmentation. Experimental results demonstrate a significant improvement in the performance of the fine-tuned SAM.
@article{arxiv.2306.14109,
title = {When SAM Meets Sonar Images},
author = {Lin Wang and Xiufen Ye and Liqiang Zhu and Weijie Wu and Jianguo Zhang and Huiming Xing and Chao Hu},
journal= {arXiv preprint arXiv:2306.14109},
year = {2023}
}