Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/10225
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dc.contributor.authorRohilla, Saransh-
dc.contributor.authorYadav, Sannidhya-
dc.contributor.authorPuthooran, Emjee [Guided by]-
dc.date.accessioned2023-10-05T09:42:01Z-
dc.date.available2023-10-05T09:42:01Z-
dc.date.issued2023-
dc.identifier.urihttp://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/10225-
dc.descriptionEnrollment No. 191008, 191023en_US
dc.description.abstractAutomated driving and driver assistance systems heavily rely on accurate traffic sign recognition. This involves two steps: detection and classification, which require sophisticated vision algorithms due to the diverse visual characteristics of traffic sign images. Researchers are actively working on developing novel methods to tackle this challenging problem. Traffic sign recognition is crucial for self-driving cars as it enables them to understand the traffic environment and make informed decisions based on road signs and markings.en_US
dc.language.isoen_USen_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectTraffic sign recognition systemen_US
dc.subjectAutonomous vehicleen_US
dc.subjectMachine learningen_US
dc.subjectConvolutional neural networksen_US
dc.titleTraffic Sign Recognition System for Autonomous Vehicleen_US
dc.typeProject Reporten_US
Appears in Collections:B.Tech. Project Reports

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