  Robotics applications in urban environments are subject to obstacles that
exhibit specular reflections hampering autonomous navigation. On the other
hand, these reflections are highly polarized and this extra information can
successfully be used to segment the specular areas. In nature, polarized light
is obtained by reflection or scattering. Deep Convolutional Neural Networks
(DCNNs) have shown excellent segmentation results, but require a significant
amount of data to achieve best performances. The lack of data is usually
overcomed by using augmentation methods. However, unlike RGB images,
polarization images are not only scalar (intensity) images and standard
augmentation techniques cannot be applied straightforwardly. We propose to
enhance deep learning models through a regularized augmentation procedure
applied to polarimetric data in order to characterize scenes more effectively
under challenging conditions. We subsequently observe an average of 18.1%
improvement in IoU between non augmented and regularized training procedures on
real world data.
