提出自适应多光谱融合框架，提升复杂光照下行人检测精度
PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian Detection
- 通过多尺度光谱感知模块自适应融合可见光与红外特征
- 在低光照条件下实现mAP提升6.6%，显著增强检测稳定性
- 适合智能交通中需跨模态鲁棒检测的场景
智能交通系统中的行人检测虽取得进展，但仍面临两大挑战：一是可见光与红外光谱间互补信息融合不足，尤其在复杂场景下；二是对光照变化敏感，如低光或过曝条件导致性能下降。为此，我们提出PedDet，一种专为多光谱行人检测优化的自适应光谱互补性框架。PedDet引入多尺度光谱特征感知模块（MSFPM），自适应融合可见光与红外特征，增强特征提取的鲁棒性与灵活性；同时设计光照鲁棒特征解耦模块（IRFDM），通过分离行人与背景特征提升不同光照下的检测稳定性；进一步采用对比对齐机制强化跨模态特征区分能力。在LLVIP和MSDS数据集上的实验表明，PedDet达到当前最优性能，mAP提升6.6%，即使在低光照条件下仍保持高精度，为道路安全提供重要进展。代码将公开于https://github.com/AIGeeksGroup/PedDet。
原文摘要 · Abstract (English)
Pedestrian detection in intelligent transportation systems has made significant progress but faces two critical challenges: (1) insufficient fusion of complementary information between visible and infrared spectra, particularly in complex scenarios, and (2) sensitivity to illumination changes, such as low-light or overexposed conditions, leading to degraded performance. To address these issues, we propose PedDet, an adaptive spectral optimization complementarity framework specifically enhanced and optimized for multispectral pedestrian detection. PedDet introduces the Multi-scale Spectral Feature Perception Module (MSFPM) to adaptively fuse visible and infrared features, enhancing robustness and flexibility in feature extraction. Additionally, the Illumination Robustness Feature Decoupling Module (IRFDM) improves detection stability under varying lighting by decoupling pedestrian and background features. We further design a contrastive alignment to enhance intermodal feature discrimination. Experiments on LLVIP and MSDS datasets demonstrate that PedDet achieves state-of-the-art performance, improving the mAP by 6.6% with superior detection accuracy even in low-light conditions, marking a significant step forward for road safety. Code will be available at https://github.com/AIGeeksGroup/PedDet.
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