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English(EN) AdaFuse-Det: Adaptive Cross-Modal Fusion of Event Cameras for Robust Object Detection in Low-Light RGB Imagery

新框架融合RGB和事件相机以实现低光照目标检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了AdaFuse-Det，一个旨在改善极低光照条件下目标检测性能的新型双流框架。该系统使用自适应跨模态融合模块，将增强的RGB图像与事件相机数据融合。该框架在理论上将融合策略建立在最小方差线性估计的基础上，旨在恢复最优融合权重。在LLE-VOS基准测试中，AdaFuse-Det展示了显著的性能提升，在严重的光照退化下实现了65.54%的召回率。 AI

影响 提高了在具有挑战性的低光照环境下的目标检测能力，可能使自主系统和监控受益。

排序理由 这是一篇详细介绍新目标检测方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新框架融合RGB和事件相机以实现低光照目标检测

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这是一篇详细介绍新目标检测方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Raju Imandi, Chethana B, Bharatesh Chakravarthi, Yong-Guk Kim, Manipriya S, Pavan Kumar B N ·

    AdaFuse-Det：用于低光RGB图像鲁棒目标检测的事件相机自适应跨模态融合

    arXiv:2605.24691v1 Announce Type: new Abstract: Detecting objects reliably under extreme low-light conditions is an open problem in computer vision, with practical urgency in applications ranging from nighttime surveillance to search-and-rescue robotics. Conventional RGB cameras …

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