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

dc.contributor.authorBibi, Rozi
dc.contributor.authorSaeed, Yousaf
dc.contributor.authorZeb, Asim
dc.contributor.authorGhazal, Taher M.
dc.contributor.authorRahman, Taj
dc.contributor.authorSaid, Raed A.
dc.contributor.authorAbbas, Sagheer
dc.contributor.authorAhmad, Munir
dc.contributor.authorKhan, Muhammad Adnan
dc.date.copyright© 2021
dc.descriptionThis article is licensed under Creative Commons License and full text is openly accessible in CUD Digital Repository. The version of the scholarly record of this article is published in Computational Intelligence and Neuroscience (2021), accessible online through this link https://doi.org/10.1155/2021/6262194en_US
dc.description.abstractRoad 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.identifier.citationBibi, 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. https://doi.org/10.1155/2021/6262194en_US
dc.publisherHindawi Limiteden_US
dc.relationAuthors 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.ispartofseriesComputational Intelligence and Neuroscience;Volume 2021
dc.rightsCreative Commons Attribution License
dc.rights.holderCopyright : © 2021 Rozi Bibi et al.
dc.subjectAnomaly detectionen_US
dc.subjectClassification (of information)en_US
dc.subjectClimate changeen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDeep learningen_US
dc.subjectIntelligent systemsen_US
dc.subjectIntelligent vehicle highway systemsen_US
dc.subjectRoads and streetsen_US
dc.subjectSurface defectsen_US
dc.subjectVehicular ad hoc networksen_US
dc.subjectAutomated detection and classificationen_US
dc.subjectAutomatic Detectionen_US
dc.subjectAutonomous Vehiclesen_US
dc.subjectHeavier vehiclesen_US
dc.subjectLow qualitiesen_US
dc.subjectMechanical faultsen_US
dc.subjectRoad surfacesen_US
dc.subjectSurface anomaliesen_US
dc.subjectTraffic flowen_US
dc.subjectVehicular Adhoc Networks (VANETs)en_US
dc.titleEdge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learningen_US
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