English(EN) SonarLLM: A Native Sonar--Optical Multimodal Large Language Model for Underwater Perception

新型SonarLLM模型通过原生声纳处理增强水下感知能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-26 04:00

研究人员开发了SonarLLM，这是一种新颖的多模态大语言模型，专为水下感知而设计。与主要依赖光学数据的现有模型不同，SonarLLM原生处理声纳输入，使其能够根据不断变化的环境条件自适应地利用声纳和光学数据。该模型与用于评估水下感知任务的基准数据集SonarBench一同推出。在测试中，SonarLLM的性能显著优于基线模型，在识别、计数、视觉问答和字幕生成等任务中表现出色，尤其是在充满挑战的浑浊水下环境中。 AI

影响这项研究通过改善在挑战性条件下的感知能力，有望为水下探索和机器人技术带来更强大的AI系统。

排序理由该集群包含一篇详细介绍新模型和基准的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Cong Su, longxuan ma, Ling Dong, Guofeng Tang, Weijie Yin, Haohui Chen, Zhengtao Yu · 2026-08-26 04:00

SonarLLM：一种用于水下感知的原生声纳-光学多模态大语言模型

arXiv:2608.24325v1 Announce Type: new Abstract: Reliable underwater perception requires complementary sensing under variable visibility. Optical cameras capture appearance and semantics but degrade rapidly with turbidity, whereas imaging sonar preserves geometry while exhibiting …

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SonarLLM：一种用于水下感知的原生声纳-光学多模态大语言模型

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