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Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada

Sharma Teena, Chehri Abdellah, Fofana Issouf, Jadhav Shubham, Khare Siddhartha, Debaque Benoit, Duclos-Hindie Nicolas et Arya Deeksha. (2024). Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada. IEEE Access, 12, p. 13648-13662.

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URL officielle: http://dx.doi.org/10.1109/ACCESS.2024.3354076

Résumé

The rapid development of self-driving vehicles requires integrating a sophisticated sensing system to address the various obstacles posed by road traffic efficiently. While several datasets are available to support object detection in autonomous vehicles, it is crucial to carefully evaluate the suitability of these datasets for different weather conditions across the globe. In response to this requirement, we present a novel dataset named the Canadian Vehicle Datasets (CVD). Subsequently, we present deep learning models that use this dataset. The CVD comprises street-level videos which were recorded by Thales, Canada. These videos were collected with high-quality cameras mounted on a vehicle in the Canadian province of Quebec. The recordings were made during daytime and nighttime, capturing weather conditions such as hazy, snowy, rainy, gloomy, nighttime and sunny days. A total of 10000 images of vehicles and other road assets are extracted from the collected videos. A total of 8388 images were annotated with corresponding generated labels 27766 with their respective 11 different classes. We analyzed the performance of the YOLOv8 model trained using the existing RoboFlow dataset. Then, we compared it with the model trained on the expanded version of RoboFlow using the proposed weather-specific dataset, CVD. Final values of improved accuracy of 73.26 %, 72.84 %, and 73.47 % (Precision/Recall/mAP) were reported upon adding the proposed dataset. Finally, the model trained on this diverse dataset exhibits heightened robustness and proves highly beneficial for both autonomous and conventional vehicle operations, making it applicable not only in Canada but also in other countries with comparable weather conditions.

Type de document:Article publié dans une revue avec comité d'évaluation
ISSN:2169-3536
Volume:12
Pages:p. 13648-13662
Version évaluée par les pairs:Oui
Date:2024
Nombre de pages:15
Identifiant unique:10.1109/ACCESS.2024.3354076
Sujets:Sciences naturelles et génie > Génie
Sciences naturelles et génie > Génie > Génie électrique et génie électronique
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
Unités de recherche > Centre international de recherche sur le givrage atmosphérique et l’ingénierie des réseaux électriques (CENGIVRE) > Vieillissement de l’appareillage installé sur les lignes à haute tension (ViAHT)
Mots-clés:meteorology, object recognition, autonomous vehicles, YOLO, computational modeling, training, roads, convolutional neural networks, intelligent transportation systems, surveillance, météorologie, reconnaissance d'objets, véhicules autonomes, modélisation informatique, formation, routes, réseaux de neurones convolutifs, systèmes de transport intelligents
Déposé le:02 févr. 2024 19:32
Dernière modification:02 févr. 2024 19:32
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