When SAM Meets Sonar Images
Lin Wang Xiufen Ye Liqiang Zhu Weijie Wu Jianguo Zhang Huiming Xing Chao Hu
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.