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

Yang Song¹, Xin Zuo¹, Chenyu Qu¹

¹School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China.

Journal of Imaging

|June 25, 2026

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This study introduces a lightweight RGB-thermal fusion method for robust pedestrian detection in challenging conditions. The novel approach enhances feature representation and fusion, improving accuracy and efficiency for real-time applications.

Area of Science:

Computer Vision
Machine Learning
Sensor Fusion

Background:

Pedestrian detection is difficult in low light, high thermal contrast, and cluttered scenes.
Existing lightweight fusion methods struggle with scale variations and modality bias, impacting accuracy.

Purpose of the Study:

To develop an efficient and accurate lightweight RGB-thermal fusion pipeline for robust pedestrian detection.
To improve feature representation and fusion control in multispectral pedestrian detection.

Main Methods:

A stage-wise fusion pipeline incorporating Multi-scale Feature Refinement (MSFR) for pre-fusion enhancement.
Utilized Cross-Modality Fusion Transformer (CFT) for improved semantic correspondence between RGB and thermal data.
Implemented Adaptive Feature Recalibration (AFR) for post-fusion suppression of biased responses.

Keywords:

RGB–thermal pedestrian detection adaptive feature recalibration lightweight architecture multi-scale feature refinement multispectral pedestrian detection small-object detection

Related Experiment Videos

Main Results:

The proposed method demonstrates a superior accuracy-efficiency trade-off compared to lightweight baselines.
Consistent improvements were observed across multiple public RGB-thermal pedestrian detection benchmarks.
The pipeline maintains a compact architecture suitable for real-time inference.

Conclusions:

The developed lightweight fusion pipeline effectively addresses challenges in multispectral pedestrian detection.
The integration of MSFR, CFT, and AFR modules enhances robustness and accuracy.
The method offers a practical solution for real-time pedestrian detection in complex environments.