Authors:
Rachel Blin
1
;
Samia Ainouz
1
;
Stéphane Canu
1
and
Fabrice Meriaudeau
2
Affiliations:
1
Normandie Univ., INSA Rouen, LITIS, 76000 Rouen, France
;
2
University of Burgundy, UBFC, ImViA, 71200 Le Creusot, France
Keyword(s):
Road Scene, Object Detection, Adverse Weather Conditions, Polarimetric Imaging, Data Fusion, Deep Learning.
Abstract:
Autonomous vehicles and ADAS systems require a reliable road scene analysis to guarantee road users’ safety. While most of autonomous systems provide an accurate road objects detection in good weather conditions, there are still some improvements to be made when visibility is altered. Polarimetric features combined with color-based ones have shown great performances in enhancing road scenes under fog. The question remains to generalize these results to other adverse weather situations. To this end, this work experimentally compares the behaviour of the polarimetric intensities, the polarimetric Stokes parameters and the RGB images as well as their combination in different fog densities and under tropical rain. The different detection tasks show a significant improvement when using a relevant fusion scheme and features combination in all the studied adverse weather situations. The obtained results are encouraging regarding the use of polarimetric features to enhance road scene analysi
s under a wide range of adverse weather conditions.
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