CAR TRAFFIC SIGN RECOGNIZER USING CONVOLUTIONAL NEURAL NETWORK

Abstract

The roadside traffic signs we see every day are crucial to our safety on the road. Drivers and passengers
alike rely on them for vital data. As a result, students must learn to control their driving habits and always adhere to
the rules of the road as they are now enforced, all without endangering the safety of other motorists or pedestrians.
To help drivers be aware of and comply with traffic regulations, traffic sign classification systems are used to
identify and categorize signs. The suggested method addresses several of the issues with previously utilized
categorization systems, such as inaccurate predictions, expensive hardware, and frequent maintenance. The
suggested method uses a convolutional neural network to create a traffic indicators categorization algorithm. In
addition, it has a function that can identify a traffic signal from a webcam. This will allow the motorist to keep the
traffic sign in plain sight without having to constantly look up from the screen.