用框提示提升SAM2火情分割效果，适配移动端实时部署。
Promptable Fire Segmentation: Unleashing SAM2's Potential for Real-Time Mobile Deployment with Strategic Bounding Box Guidance
- 采用框提示策略优化SAM2火情分割，显著提升精度。
- 框+多点提示达0.64平均IoU（Khan数据集），优于其他方法。
- 轻量模型如TinySAM适合边缘设备，兼顾速度与准确率。
火情分割因火焰边界不规则、边缘透明及亮度变化大而具挑战性。尽管分段任意模型（SAM和SAM2）展现出强大跨域泛化能力，其在火情分割中的实际应用——尤其在移动端部署限制下——仍待探索。本文首次系统评估了SAM2变体在火情分割中的表现，聚焦框提示策略以提升部署可行性。我们对四个SAM2.1变体（tiny、small、base_plus、large）及移动端优化版本（TinySAM、MobileSAM）在三个火情数据集上进行了测试，采用多种提示方式：自动提示、单正点（SP）、单正点+单负点（SP+SN）、多正点（MP）、框提示（Box），以及混合方案（Box+SP、Box+MP）。实验表明，框提示始终优于自动或单点提示，其中Box+MP在Khan数据集上达到最高平均IoU（0.64）和Dice系数（0.75）。轻量级模型如TinySAM和MobileSAM进一步降低内存与计算开销，更适用于延迟容忍的边缘场景。本研究为火情监测系统中可提示分割模型的部署提供关键洞见，并建立领域特定SAM应用的基准。代码已开源：https://github.com/UEmmanuel5/ProFSAM。
原文摘要 · Abstract (English)
Fire segmentation remains a critical challenge in computer vision due to flames' irregular boundaries, translucent edges, and highly variable intensities. While the Segment Anything Models (SAM and SAM2) have demonstrated impressive cross-domain generalization capabilities, their effectiveness in fire segmentation -- particularly under mobile deployment constraints -- remains largely unexplored. This paper presents the first comprehensive evaluation of SAM2 variants for fire segmentation, focusing on bounding box prompting strategies to enhance deployment feasibility. We systematically evaluate four SAM2.1 variants (tiny, small, base_plus, large) alongside mobile-oriented variants (TinySAM, MobileSAM) across three fire datasets using multiple prompting strategies: automatic, single positive point (SP), single positive point + single negative point (SP+SN), multiple positive points (MP), bounding box (Box), and hybrid variants (Box+SP and Box+MP). Our experimental results demonstrate that bounding box prompts consistently outperform automatic and single point-based approaches, with Box+MP achieving the highest mean IoU (0.64) and Dice coefficient (0.75) on the Khan dataset. Lightweight variants such as TinySAM and MobileSAM further reduce memory and computational costs, making them more suitable for latency-tolerant edge scenarios. Overall, this work provides critical insights for deploying promptable segmentation models in fire monitoring systems and establishes benchmarks for future research in domain-specific SAM applications. Code is available at: https://github.com/UEmmanuel5/ProFSAM
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。