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Communication Dans Un Congrès Année : 2020

Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks

Résumé

Multispectral images (e.g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e.g. day/night outdoor scenes). To effectively use the different spectra, the main technical problem resides in the information fusion process. In this paper, we propose a new halfway feature fusion method for neural networks that leverages the complementary/consistency balance existing in multispectral features by adding to the network architecture, a particular module that cyclically fuses and refines each spectral feature. We evaluate the effectiveness of our fusion method on two challenging multispectral datasets for object detection. Our results show that implementing our Cyclic Fuse-and-Refine module in any network improves the performance on both datasets compared to other state-of-the-art multispectral object detection methods.
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Dates et versions

hal-02872132 , version 1 (17-06-2020)

Identifiants

  • HAL Id : hal-02872132 , version 1

Citer

Heng Zhang, Elisa Fromont, Sébastien Lefèvre, Bruno Avignon. Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks. ICIP 2020 - IEEE International Conference on Image Processing, Oct 2020, Abou Dabi, United Arab Emirates. pp.1-5. ⟨hal-02872132⟩
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