Deep Learning Based Traffic Signs Boundary Estimation
Résumé
In the context of autonomous navigation, the localization of the vehicle relies on the accurate detection and tracking of artificial landmarks. These landmarks are based on handcrafted features. However, because of their low- level nature, they are not informative but also not robust under various conditions (lightning, weather, point-of-view). Moreover, in Advanced Driver-Assistance Systems (ADAS), and road safety, intense efforts have been made to implement automatic visual data processing, with special emphasis on road object recognition. The main idea of this work is to detect accurate higher-level landmarks such as static semantic objects using Deep learning frameworks. We mainly focus on the accurate detection, segmentation and classification of vertical traffic signs according to their function (danger, give way, prohibition/obligation, and indication). This paper presents the boundary estimation of European traffic signs from an embedded monocular camera in a vehicle. We propose a framework using two different deep neural networks in order to: (1) detect and recognize traffic signs in the video flow and (2) regress the coordinates of each vertices of the detected traffic sign to estimate its shape boundary. We also provide a comparison of our method with Mask R-CNN [1] which is the state-of-the-art segmentation method.
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