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Deep Learning Based Traffic Signs Boundary Estimation

Hrustic, Emir and Xu, Zhujun and Vivet, Damien Deep Learning Based Traffic Signs Boundary Estimation. (2020) In: 2020 IEEE Intelligent Vehicles Symposium (IV), 19 October 2020 - 15 November 2020 (Las Vegas, United States).

(Document in English)

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Official URL: https://doi.org/10.1109/IV47402.2020.9304590


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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to the IEEE (Institute of Electrical and Electronics Engineers). This paper is available at : https://ieeexplore.ieee.org/document/9304590 “© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
HAL Id:hal-03194086
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Laboratory name:
ANR 10.13039/501100001665 - FUI 10.13039/501100003391
Deposited On:09 Apr 2021 09:59

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