When SAM Meets Sonar Images

• URL: http://arxiv.org/abs/2306.14109v1
• Date: Sun, 25 Jun 2023 03:15:14 GMT
• Title: When SAM Meets Sonar Images
• Authors: Lin Wang, Xiufen Ye, Liqiang Zhu, Weijie Wu, Jianguo Zhang, Huiming Xing, Chao Hu
• Abstract summary: Segment Anything Model (SAM) has revolutionized the way of segmentation. SAM's performance may decline when applied to tasks involving domains that differ from natural images. By employing fine-tuning techniques, SAM exhibits promising capabilities in specific domains, such as medicine and planetary science.
• Score: 6.902760999492406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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