Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy.


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    Title :

    Traffic Sign Detection Using YOLOv8


    Contributors:
    Kumar, Rahul (author) / Gupta, Aniket (author) / D, Rajeswari (author)


    Publication date :

    2024-01-04


    Size :

    905302 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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