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Traffic Signs Detection and Recognition System in Snowy Environment Using Deep Learning

Chehri Hamou, Chehri Abdellah et Saadane Rachid. (2020). Traffic Signs Detection and Recognition System in Snowy Environment Using Deep Learning. Dans : The Proceedings of the Third International Conference on Smart City Applications, SCA 2020: Innovations in Smart Cities Applications Volume 4 , 7-9 october, 2020, Karabuk, Turkey.

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A fully autonomous car does not yet exist. But the vehicles have continued to gain in range in recent years. The main reason? The dazzling progress made in artificial intelligence, in particular by specific algorithms, known as machine learning. These example-based machine learning methods are used in particular for recognizing objects in photos. The algorithms developed for the detection and identification must respond robustly to the various disturbances observed and take into account the variability in the signs’ appearance. Variations in illumination generate changes in apparent color, shadows, reflections, or backlighting. Besides, geometric distortions or rotations may appear depending on the viewing angle and the panels’ scale. Their appearance may also vary depending on their state of wear and possible dirt, damage. In this work, to improve the accuracy of detection and classification of sign road partially covered by snow, we use the Fast Region-based Convolutional Network method (Fast R-CNN) model. To train the detection model, we collect an image dataset composed of multi-class of road signs. Our model can simultaneously multi-class of a road sign in nearly real-time.

Type de document:Matériel de conférence (Non spécifié)
Sujets:Sciences naturelles et génie > Génie
Sciences naturelles et génie > Génie > Génie informatique et génie logiciel
Sciences naturelles et génie > Sciences appliquées
Département, module, service et unité de recherche:Départements et modules > Département des sciences appliquées > Module d'ingénierie
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Mots-clés:deep learning, automatic classification, traffic sign, detection, apprentissage en profondeur, classification automatique, panneau de signalisation, détection
Déposé le:19 avr. 2022 18:31
Dernière modification:19 avr. 2022 18:31
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