  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.
