The importance of the traffic signs we see on the roadways in our daily lives as drivers cannot be overstated. They provide critical details to the motorist. As a consequence, drivers must progressively learn to manage their driving behaviour and ensure that they properly conform to current traffic regulations without harming other vehicles or pedestrians. Classification of traffic signs is used to identify and categorize traffic signs in order to alert and warn a vehicle in advance to prevent breaking the law. The suggested method employs a convolutional neural network to create a traffic sign classification algorithm. It also includes the capability of traffic sign webcam detection. This will let the driver to see the sign directly in front of his or her eyes on the screen, saving time from having to manually check the traffic sign each time. Therefore, in this project, we proposed a driver assistance system which can detect traffic signs and the driving lane of the vehicle. We use YOLOv5, a clever CNN to conduct traffic sign assessment and identification and an image processing technique to detect lane markings. We used the largest German traffic sign dataset, which has more than 50,000 photos, plus a dataset of 2000 traffic signs from India. Moreover, the proposed system also provides voice feedback for the detected traffic signs and an additional feature has been included in the lane detection system to calculate how far the vehicle is from the centre of lane. Our proposed model has achieved an accuracy of 97.4%. As a result, the detection process’s overall processing efficiency was increased, and it could now meet real-time processing needs.
Real-Time Traffic Signs and Lane Line Detection
Lect. Notes in Networks, Syst.
Doctoral Symposium on Computational Intelligence ; 2023 ; Lucknow, India March 03, 2023 - March 03, 2023
Proceedings of Fourth Doctoral Symposium on Computational Intelligence ; Kapitel : 71 ; 897-906
2023-09-17
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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