融合事件相机与增强图像，提升极暗环境下的目标检测精度
AdaFuse-Det: Adaptive Cross-Modal Fusion of Event Cameras for Robust Object Detection in Low-Light RGB Imagery
- 用自适应融合模块结合增强的RGB图和事件数据
- 在极端低光下达到59.12%的F1分数，召回率显著优于单一模态
- 适合夜间监控、搜救机器人等低光场景应用
在极端低光条件下可靠地检测物体是计算机视觉中的开放难题，具有从夜间监控到搜救机器人等实际应用的紧迫需求。传统RGB相机在光子通量极低时性能急剧下降，而事件相机以微秒级分辨率记录像素亮度的异步变化，具备高动态范围和几乎与光照无关的结构信息。本文提出AdaFuse-Det，一种双流框架，通过基于最小方差线性估计理论的自适应跨模态融合（ACMF）模块，将CLAHE增强的RGB帧与体素化事件张量融合。我们证明了学习到的注意力图渐近逼近高斯-马尔可夫最优融合权重，并确立了体素化阶段的事件守恒性和时间分辨率边界。在LLE-VOS基准上，AdaFuse-Det在严重光照退化下实现65.54%的召回率、53.85%的精确率和59.12%的F1分数，其召回率相比单模态检测器显著提升，符合理论预测的光照适应行为。
原文摘要 · Abstract (English)
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 degrade sharply at low photon flux, while event cameras which record asynchronous per-pixel brightness changes at microsecond resolution and high dynamic range provide complementary structural cues that are largely illumination-invariant. We present AdaFuse-Det, a dual-stream framework that fuses CLAHE-enhanced RGB frames with voxelized event tensors through an Adaptive Cross-Modal Fusion (ACMF) module grounded in minimum-variance linear estimation theory. We formally show that the learned attention map asymptotically recovers the Gauss-Markov optimal fusion weights, and establish event conservation and temporal resolution bounds for the voxelization stage. On the LLE-VOS benchmark, AdaFuse-Det achieves a Recall of $65.54\%$, Precision of $53.85\%$, and F1-Score of $59.12\%$ under severe illumination degradation, outperforming single-modality detectors in recall by a margin that reflects the theoretically predicted illumination-adaptation behavior.
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