Communication Dans Un Congrès Année : 2025

Patch based image processing for complex environment characterization

Résumé

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. Vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the sky.

Because the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy.

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Dates et versions

hal-04873379 , version 1 (08-01-2025)

Identifiants

  • HAL Id : hal-04873379 , version 1

Citer

Corentin Menier, Cyril Meurie, Timothée Guillemaille, Yassine Ruichek, Juliette Marais. Patch based image processing for complex environment characterization. Sixth IEEE International Image Processing, Applications and Systems Conference (IPAS25), Jan 2025, Lyon, France. 6p. ⟨hal-04873379⟩
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