首个高分辨率事件-可见光数据集，专为极端条件下的目标检测设计。
PEOD: A Pixel-Aligned Event-RGB Benchmark for Object Detection under Challenging Conditions
- 构建像素对齐的1280×720高清事件-可见光数据集，覆盖复杂场景。
- 57%数据含低光、过曝和高速运动，验证模型在极端条件下的性能。
- 揭示融合模型在光照严重退化时的局限性，推动多模态感知研究。
针对挑战性场景中鲁棒目标检测日益依赖事件相机的问题，现有事件-可见光数据集存在极端条件覆盖不足、空间分辨率低（≤640×480）等局限，难以全面评估检测器性能。为此，我们提出PEOD，首个大规模、像素对齐且高分辨率（1280×720）的事件-可见光目标检测数据集。PEOD包含130+个时空对齐序列和34万标注边界框，其中57%数据采集于低光、过曝及高速运动场景。我们在该数据集上对14种方法在三种输入配置（基于事件、基于可见光、事件-可见光融合）下进行基准测试。全测试集与正常子集上，融合模型表现优异；但在光照挑战子集，顶尖事件模型优于所有融合模型，而融合模型仍胜过纯可见光模型，表明现有融合方法在某一模态严重退化时存在瓶颈。PEOD为多模态感知提供了真实、高质量的评估基准，助力未来研究。
原文摘要 · Abstract (English)
Robust object detection for challenging scenarios increasingly relies on event cameras, yet existing Event-RGB datasets remain constrained by sparse coverage of extreme conditions and low spatial resolution (<= 640 x 480), which prevents comprehensive evaluation of detectors under challenging scenarios. To address these limitations, we propose PEOD, the first large-scale, pixel-aligned and high-resolution (1280 x 720) Event-RGB dataset for object detection under challenge conditions. PEOD contains 130+ spatiotemporal-aligned sequences and 340k manual bounding boxes, with 57% of data captured under low-light, overexposure, and high-speed motion. Furthermore, we benchmark 14 methods across three input configurations (Event-based, RGB-based, and Event-RGB fusion) on PEOD. On the full test set and normal subset, fusion-based models achieve the excellent performance. However, in illumination challenge subset, the top event-based model outperforms all fusion models, while fusion models still outperform their RGB-based counterparts, indicating limits of existing fusion methods when the frame modality is severely degraded. PEOD establishes a realistic, high-quality benchmark for multimodal perception and facilitates future research.
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