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Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning

Show simple item record Bibi, Rozi Saeed, Yousaf Zeb, Asim Ghazal, Taher M. Rahman, Taj Said, Raed A. Abbas, Sagheer Ahmad, Munir Khan, Muhammad Adnan 2021-11-03T11:55:53Z 2021-11-03T11:55:53Z © 2021 2021
dc.identifier.citation Bibi, R., Saeed, Y., Zeb, A., Ghazal, T. M., Rahman, T., Said, R. A., . . . Khan, M. A. (2021). Edge AI-based automated detection and classification of road anomalies in VANET using deep learning. Computational Intelligence and Neuroscience, 2021. en_US
dc.identifier.issn 16875265
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in Computational Intelligence and Neuroscience (2021), available online at: en_US
dc.description.abstract Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification. © 2021 Rozi Bibi et al. en_US
dc.language.iso en en_US
dc.publisher Hindawi Limited en_US
dc.relation Authors Affiliations : Bibi, R., Department of Information Technology, University of Haripur, Haripur, Pakistan; Saeed, Y., Department of Information Technology, University of Haripur, Haripur, Pakistan; Zeb, A., Department of Computer Science, Abbottabad University of Science and Technology, Havelian, Pakistan; Ghazal, T.M., Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM, Bangi, Selangor 43600, Malaysia, School of Information Technology, Skyline University College, University City Sharjah, Sharjah, 1797, United Arab Emirates; Rahman, T., Department of Physical and Numerical Science, Qurtuba University of Science and Information Technology, Peshawar, 25000, Pakistan; Said, R.A., Canadian University Dubai, Dubai, United Arab Emirates; Abbas, S., School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan; Ahmad, M., School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan; Khan, M.A., Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13120, South Korea
dc.relation.ispartofseries Computational Intelligence and Neuroscience;Volume 2021
dc.rights Creative Commons Attribution License
dc.subject Anomaly detection en_US
dc.subject Classification (of information) en_US
dc.subject Climate change en_US
dc.subject Convolutional neural networks en_US
dc.subject Deep learning en_US
dc.subject Intelligent systems en_US
dc.subject Intelligent vehicle highway systems en_US
dc.subject Roads and streets en_US
dc.subject Surface defects en_US
dc.subject Vehicular ad hoc networks en_US
dc.subject Automated detection and classification en_US
dc.subject Automatic Detection en_US
dc.subject Autonomous Vehicles en_US
dc.subject Heavier vehicles en_US
dc.subject Low qualities en_US
dc.subject Mechanical faults en_US
dc.subject Road surfaces en_US
dc.subject Surface anomalies en_US
dc.subject Traffic flow en_US
dc.subject Vehicular Adhoc Networks (VANETs) en_US
dc.title Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning en_US
dc.type Article en_US
dc.rights.holder Copyright : © 2021 Rozi Bibi et al.

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