  Multispectral images consisting of aligned visual-optical (VIS) and thermal
infrared (IR) image pairs are well-suited for practical applications like
autonomous driving or visual surveillance. Such data can be used to increase
the performance of pedestrian detection especially for weakly illuminated,
small-scaled, or partially occluded instances. The current state-of-the-art is
based on variants of Faster R-CNN and thus passes through two stages: a
proposal generator network with handcrafted anchor boxes for object
localization and a classification network for verifying the object category. In
this paper we propose a method for effective and efficient multispectral fusion
of the two modalities in an adapted single-stage anchor-free base architecture.
We aim at learning pedestrian representations based on object center and scale
rather than direct bounding box predictions. In this way, we can both simplify
the network architecture and achieve higher detection performance, especially
for pedestrians under occlusion or at low object resolution. In addition, we
provide a study on well-suited multispectral data augmentation techniques that
improve the commonly used augmentations. The results show our method's
effectiveness in detecting small-scaled pedestrians. We achieve 5.68%
log-average miss rate in comparison to the best current state-of-the-art of
7.49% (25% improvement) on the challenging KAIST Multispectral Pedestrian
Detection Benchmark.
  Code: https://github.com/HensoldtOptronicsCV/MultispectralPedestrianDetection
