Self driving cars are picking up pace and thus serves as a challenging research domain. One of the most crucial functions in an autonomous vehicle is accurately detecting and recognising the traffic signs. Traffic sign detection is a crucial task in traffic sign recognition systems. Deep neural networks are proven powerful in traffic sign classification. As traffic sign violation leads to law and order disruption, posing a threat to human life, there is a need for robust algorithms with efficient actuation to perform the delegated task. Thus, this paper proposes a robust algorithm which addresses challenges like haze, fog, unclear images due to improper condition of roads to efficiently detect and recognize traffic signs using Image manipulation, Optical Character Recognition (OCR) algorithm and You Only Look Once (YOLOv3) detection algorithm. The proposed algorithm in this paper for hazy images that aids in ADAS applications has an accuracy of 87.29%. This contributes to the emerging research domains of autonomous vehicles and self-driving cars.


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    Titel :

    Traffic Sign Detection and Recognition for Hazy Images: ADAS


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Chen, Joy Iong-Zong (Herausgeber:in) / Tavares, João Manuel R. S. (Herausgeber:in) / Iliyasu, Abdullah M. (Herausgeber:in) / Du, Ke-Lin (Herausgeber:in) / Galgali, Raiee (Autor:in) / Punagin, Sahana (Autor:in) / Iyer, Nalini (Autor:in)

    Kongress:

    International Conference on Image Processing and Capsule Networks ; 2021 ; Bangkok, Thailand May 27, 2021 - May 28, 2021



    Erscheinungsdatum :

    10.09.2021


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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