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    Sum fusion of visible and infrared camera data significantly improves multispectral pedestrian detection. This study introduces a unified framework for joint semantic segmentation and detection, outperforming current methods.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Sensor Fusion

    Background:

    • Multi-modal sensor fusion, combining visible and infrared data, enhances pedestrian detection in diverse conditions.
    • Current fusion architectures for joint semantic segmentation and pedestrian detection require optimization.

    Purpose of the Study:

    • To identify the optimal fusion strategy for multispectral information in joint semantic segmentation and pedestrian detection.
    • To develop a unified framework for training semantic segmentation and target detection models concurrently.

    Main Methods:

    • Investigated various fusion architectures for multispectral pedestrian detection.
    • Compared sum fusion and concatenation fusion strategies.
    • Utilized two-stream semantic segmentation for feature learning supervision.

    Main Results:

    • Sum fusion outperformed other strategies, including concatenation fusion, for multispectral detection.
    • Two-stream semantic segmentation without fusion proved most effective for semantic supervision.
    • The proposed unified framework achieved state-of-the-art results on the KAIST benchmark.

    Conclusions:

    • The sum fusion strategy is optimal for multispectral pedestrian detection.
    • A unified framework integrating semantic segmentation and detection enhances performance.
    • This research advances robust pedestrian detection using multispectral fusion.