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Proceedings Paper

Adapted learning for polarization-based car detection
Author(s): Rachel Blin; Samia Ainouz; Stéphane Canu; Fabrice Meriaudeau
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Paper Abstract

Object detection in road scenes is an unavoidable task to develop autonomous vehicles and driving assistance systems. Deep neural networks have shown great performances using conventional imaging in ideal cases but they fail to properly detect objects in case of unstable scenes such as high reflections, occluded objects or small objects. Next to that, Polarized imaging, characterizing the light wave, can describe an object not only by its shape or color but also by its reflection properties. That feature is a reliable indicator of the physical nature of the object even under poor illumination or strong reflections. In this paper, we show how polarimetric images, combined with deep neural networks, contribute to enhance object detection in road scenes. Experimental results illustrate the effectiveness of the proposed framework at the end of this paper.

Paper Details

Date Published: 16 July 2019
PDF: 7 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 1117218 (16 July 2019); doi: 10.1117/12.2523388
Show Author Affiliations
Rachel Blin, Normandie Univ., INSA Rouen (France)
Samia Ainouz, Normandie Univ., INSA Rouen (France)
Stéphane Canu, Normandie Univ., INSA Rouen (France)
Fabrice Meriaudeau, Univ. de Bourgogne (France)


Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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