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Updated: Jan 14, 2026

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Implicit Illumination-Aware Representation With Cross-Modal Prefusion Alignment for Universal Multispectral

Yan Gong, Lei Lin, Yang Luo

    IEEE Transactions on Neural Networks and Learning Systems
    |January 12, 2026
    PubMed
    Summary

    This study introduces a universal multispectral pedestrian detection paradigm (UMPDP) to improve all-day pedestrian detection. The UMPDP effectively fuses red-green-blue (RGB) and thermal images, overcoming limitations in adverse illumination conditions.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Traditional pedestrian detection using red-green-blue (RGB) images is limited in adverse illumination conditions, hindering critical applications like security and autonomous driving.
    • Multispectral pedestrian detection, incorporating thermal imaging, offers a solution by enabling detection independent of external light sources.
    • Effective fusion strategies for RGB and thermal data remain an underexplored area, presenting challenges in cross-modal interaction and feature fusion.

    Purpose of the Study:

    • To develop an advanced multispectral pedestrian detection method capable of robust all-day detection.
    • To address limitations in existing methods, including inadequate illumination awareness and spatial misalignment between modalities.
    • To propose a novel fusion paradigm that enhances cross-modal interactions and optimizes feature fusion.

    Main Methods:

    • Proposed an implicit illumination-aware representation to overcome the scarcity of illumination-specific labels in datasets.
    • Introduced a prefusion feature alignment strategy to correct spatial misalignments across RGB and thermal images.
    • Developed a Universal Multispectral Pedestrian Detection Paradigm (UMPDP) comprising a Modality Alignment Module (MAM), Differential Modality Fusion Module (DMFM), and Task-Conditioned Illumination Module (TCIM).

    Main Results:

    • The UMPDP demonstrated significant improvements in multispectral pedestrian detection across benchmark datasets (KAIST and CVC-14).
    • The proposed modules effectively addressed challenges related to illumination variations and cross-modal feature fusion.
    • The method achieved superior performance compared to existing multispectral pedestrian detectors.

    Conclusions:

    • The developed Universal Multispectral Pedestrian Detection Paradigm (UMPDP) offers a robust and effective solution for all-day pedestrian detection.
    • The proposed fusion strategies and illumination-aware modules significantly enhance the performance of multispectral pedestrian detection systems.
    • This work provides a strong foundation for future research in multispectral object detection, particularly in challenging environmental conditions.