Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/15654
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dc.contributor.authorKovilpillai, Judeson Antony J-
dc.contributor.authorDhar, Soumi-
dc.contributor.authorSingh, Uday Kumar-
dc.contributor.authorPrakash, S-
dc.contributor.authorSaidala, Ravi Kumar-
dc.contributor.authorJayanthy, S-
dc.date.accessioned2024-05-29T08:51:27Z-
dc.date.available2024-05-29T08:51:27Z-
dc.date.issued2023-
dc.identifier.citationVol. 2023, No. 22; pp. 31-36en_US
dc.identifier.issn2732-4494-
dc.identifier.urihttp://dx.doi.org/10.1049/icp.2023.2849-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15654-
dc.description.abstractThe rapid expansion of autonomous driving technologies necessitates the development of robust systems for accurate road surface identification and classification to ensure safe and reliable driving. This review article addresses the imperative requirement for effective road surface classification by investigating diverse methodologies within the realms of computer vision and the Internet of Things (IoT). Through an extensive investigation, various techniques encompassing Image processing, Machine Learning(ML), Deep Learning(DL), and IoT are examined for their effectiveness in classifying road surfaces at different terrains. Moreover, this article also reviews datasets, signals, sensors, communication protocols, IoT implementation strategies, pre-processing methodologies, and feature extraction techniques. This investigation delves into novel approaches aimed at resolving road surface classification challenges, meticulously examining their respective strengths and limitations. Furthermore, this article includes a comparative analysis of these advanced methodologies, facilitating the identification of the most suitable model for the task. The assessment takes into account intricate methodological aspects, types of sensors/cameras, dataset variations, and performance metrics, thereby providing valuable insights into the landscape of road surface classification for autonomous driving applications. © The Institution of Engineering & Technology 2023.en_US
dc.language.isoenen_US
dc.publisherIET Conference Proceedingsen_US
dc.publisherInstitution of Engineering and Technologyen_US
dc.subjectAutonomous Vehiclesen_US
dc.subjectClassification (Of Information)en_US
dc.subjectComputer Visionen_US
dc.subjectDeep Learningen_US
dc.subjectRoads And Streetsen_US
dc.subjectAutonomous Drivingen_US
dc.subjectImages Processingen_US
dc.titleAdvancements in Road Surface Classification: A Comprehensive Review of Computer Vision and Iot Approachesen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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