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.
Traffic Sign Detection and Recognition for Hazy Images: ADAS
Lect. Notes in Networks, Syst.
International Conference on Image Processing and Capsule Networks ; 2021 ; Bangkok, Thailand May 27, 2021 - May 28, 2021
Second International Conference on Image Processing and Capsule Networks ; Kapitel : 55 ; 650-661
10.09.2021
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
The Concept of Fusion for Clear Vision of Hazy Roads in ADAS
Springer Verlag | 2020
|ADAS METHOD FOR ESTIMATING TRAFFIC DENSITY USING ADAS PROBE DATA
Europäisches Patentamt | 2022
|ADAS CONTROL SERVER FOR ESTIMATING TRAFFIC DENSITY USING ADAS PROBE DATA
Europäisches Patentamt | 2021
|Traffic Sign Detection and Recognition
Springer Verlag | 2017
|