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Infusion-Net: Inter- and Intra-Weighted Cross-Fusion Network for Multispectral Object Detection

Author

Listed:
  • Jun-Seok Yun

    (Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea)

  • Seon-Hoo Park

    (Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea)

  • Seok Bong Yoo

    (Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea)

Abstract

Object recognition is conducted using red, green, and blue (RGB) images in object recognition studies. However, RGB images in low-light environments or environments where other objects occlude the target objects cause poor object recognition performance. In contrast, infrared (IR) images provide acceptable object recognition performance in these environments because they detect IR waves rather than visible illumination. In this paper, we propose an inter- and intra-weighted cross-fusion network (Infusion-Net), which improves object recognition performance by combining the strengths of the RGB-IR image pairs. Infusion-Net connects dual object detection models using a high-frequency (HF) assistant (HFA) to combine the advantages of RGB-IR images. To extract HF components, the HFA transforms input images into a discrete cosine transform domain. The extracted HF components are weighted via pretrained inter- and intra-weights for feature-domain cross-fusion. The inter-weighted fused features are transmitted to each other’s networks to complement the limitations of each modality. The intra-weighted features are also used to enhance any insufficient HF components of the target objects. Thus, the experimental results present the superiority of the proposed network and present improved performance of the multispectral object recognition task.

Suggested Citation

  • Jun-Seok Yun & Seon-Hoo Park & Seok Bong Yoo, 2022. "Infusion-Net: Inter- and Intra-Weighted Cross-Fusion Network for Multispectral Object Detection," Mathematics, MDPI, vol. 10(21), pages 1-16, October.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3966-:d:952965
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    References listed on IDEAS

    as
    1. Jun-Seok Yun & Seok-Bong Yoo, 2022. "Single Image Super-Resolution with Arbitrary Magnification Based on High-Frequency Attention Network," Mathematics, MDPI, vol. 10(2), pages 1-19, January.
    2. Sung-Jin Lee & Jun-Seok Yun & Eung Joo Lee & Seok Bong Yoo, 2022. "HIFA-LPR: High-Frequency Augmented License Plate Recognition in Low-Quality Legacy Conditions via Gradual End-to-End Learning," Mathematics, MDPI, vol. 10(9), pages 1-24, May.
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