MSAFusion: A Lightweight Multispectral Pedestrian Detection Network with Multi-Scale and Adaptive Feature Fusion

Robust multispectral pedestrian detection remains challenging in complex environments such as those with low illumination, strong thermal contrast, and background clutter. Although RGB-thermal fusion provides complementary cues, lightweight detectors often suffer from unstable feature representation across scales and insufficient control over modality-biased responses during fusion, which can degrade localization accuracy and weaken the detection of small or distant pedestrians. To address these issues, we develop a lightweight stage-wise RGB-thermal fusion pipeline that integrates pre-fusion feature refinement, cross-modal interaction, and post-fusion adaptive recalibration. Specifically, a Multi-scale Feature Refinement (MSFR) module is proposed at the mid-level to enhance modality-specific representations by jointly modeling local details and contextual information, thereby reducing scale-sensitive noise before interaction. An established Cross-Modality Fusion Transformer (CFT) is then adopted to promote semantic correspondence between RGB and thermal features. After interaction, an Adaptive Feature Recalibration (AFR) module is introduced to suppress background-dominated and modality-biased responses through lightweight channel-wise adjustment. Extensive experiments on three public RGB-thermal benchmarks, including the pedestrian-focused KAIST and LLVIP datasets together with the FLIR-aligned road-scene benchmark, demonstrate that the proposed method achieves a favorable accuracy-efficiency trade-off, delivering consistent improvements over the lightweight baseline while maintaining a compact architecture and real-time inference capability.