首个跨谱段伪装行人检测基准数据集，助力自动驾驶安全
Camo-M3FD: A New Benchmark Dataset for Cross-Spectral Camouflaged Pedestrian Detection
- 构建可见光-热成像配对数据集，量化背景相似性确保挑战性
- 热成像仅定位有效，多模态融合才能精准还原行人结构
- 专为安全关键场景设计，适合自动驾驶与安防系统研发
行人检测是自动驾驶、机器人和监控的基础。尽管深度学习取得进展，但遮挡、复杂背景和可视性下降仍带来挑战。多谱检测（结合可见光与热成像）可缓解可视性问题，但伪装行人检测仍鲜有研究。现有伪装目标检测（COD）数据集多聚焦生物体，缺乏对安全关键的人类目标研究。为此，我们提出Camo-M3FD（基于M3FD数据集），首个跨谱段伪装行人检测基准，包含配准的可见光-热成像图像对。通过量化指标筛选，确保前景与背景高度相似。提供高质量像素级掩码，并基于前沿COD模型建立标准化评估框架。实验表明，热信号虽具关键定位能力，但多模态融合对细节重构不可或缺。Camo-M3FD为开发鲁棒、安全的关键检测系统奠定基础。数据集已公开于GitHub：https://cod-espol.github.io/Camo-M3FD/
原文摘要 · Abstract (English)
Pedestrian detection is fundamental to autonomous driving, robotics, and surveillance. Despite progress in deep learning, reliable identification remains challenging due to occlusions, cluttered backgrounds, and degraded visibility. While multispectral detection-combining visible and thermal sensors-mitigates poor visibility, the challenge of camouflaged pedestrians remains largely unexplored. Existing Camouflaged Object Detection (COD) benchmarks focus on biological species, leaving a gap in safety-critical human detection where targets blend into their surroundings. To address this, we introduce Camo-M3FD (derived from the M3FD dataset), a novel benchmark for cross-spectral camouflaged pedestrian detection, consisting of registered visible-thermal image pairs. The dataset is curated using quantitative metrics to ensure high foreground-background similarity. We provide high-quality pixel-level masks and establish a standardized evaluation framework using state-of-the-art COD models. Our results demonstrate that while thermal signals provide indispensable localization cues, multispectral fusion is essential for refining structural details. Camo-M3FD serves as a foundational resource for developing robust and safety-critical detection systems. The dataset is available on GitHub: https://cod-espol.github.io/Camo-M3FD/
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