Several types of signs in the road traffic available are nowadays to control the speed limits. Often, the drivers in heavy traffic failed to follow the traffic rules because of this busy world. In other points of view, the drivers may not have a clear knowledge about the traffic signs, and this causes many accidents and leads them to pay fine amounts. More or less a huge number of accidents may lead to death because of not following the traffic rules. All may have self‐driving cars that mean all the passengers can completely depend on the car alone for traveling. The successful way of controlling 5 levels will leads the vehicle to understand and follow all the traffic rules. In the smart world, Artificial Intelligence (AI) development helps researchers and big companies. The toughest thing is reaching accuracy in this technique of the vehicles it interrupt with the traffic signals and able to take the decisions accordingly. In this chapter, deep neural network model is built that can classify traffic signs presented in the image into different categories with new models that able to understand the traffic signs which are a very important task for all autonomous vehicles. Deep learning helps us to train the machine to predict all kinds of traffic signals with huge datasets for more accuracy. By implementing concepts we can quietly reduce the accidents, simultaneously traffic problems which helps us to avoid the pay the fine amount.
In this project, it successfully classified the traffic sign classifiers with the accuracy of 98% and also visualized how the accuracy and loss changes with time, with the best results from a simple CNN model.
Smart IoT‐Enabled Traffic Sign Recognition With High Accuracy (TSR‐HA) Using Deep Learning
2022-03-04
15 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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