  Object detection in road scenes is necessary to develop both autonomous
vehicles and driving assistance systems. Even if deep neural networks for
recognition task have shown great performances using conventional images, they
fail to detect objects in road scenes in complex acquisition situations. In
contrast, polarization images, characterizing the light wave, can robustly
describe important physical properties of the object even under poor
illumination or strong reflections. This paper shows how non-conventional
polarimetric imaging modality overcomes the classical methods for object
detection especially in adverse weather conditions. The efficiency of the
proposed method is mostly due to the high power of the polarimetry to
discriminate any object by its reflective properties and on the use of deep
neural networks for object detection. Our goal by this work, is to prove that
polarimetry brings a real added value compared with RGB images for object
detection. Experimental results on our own dataset composed of road scene
images taken during adverse weather conditions show that polarimetry together
with deep learning can improve the state-of-the-art by about 20% to 50% on
different detection tasks.
