Traffic Sign Recognition system using Machine Learning

Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we need to carefully assess the capabilities and limitations of automated traffic sign detection systems. A series of warnings about the route are conveyed by traffic signs. They keep traffic going by aidin

Project Title

Traffic Sign Recognition system using Machine Learning

Project Area of Specialization

Artificial Intelligence

Project Summary

Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we need to carefully assess the capabilities and limitations of automated traffic sign detection systems. A series of warnings about the route are conveyed by traffic signs. They keep traffic going by aiding travelers in reaching their destinations and providing them with advance notice of arrival, exit, and turn points.

The purpose of our system is to keep drivers informing the core details of the route, and traffic signs without any interruption and compromising the driver’s concentration, So that violation of rules can be reduced as possible.

This System will be alerting the driver to follow the signs by displaying the relative information on the windscreen.

For this purpose state-of-the-art machine learning tools will be used to recognize traffic signs from distance with good efficiency.

Project Objectives

The objectives of this project include:

• The improvement of driver comfort for driving on long routes.
• Traffic safety and rules/regulations.
• Develop hardware for an automatic sign board recognition system to alert drivers without any distractions.
• A night vision camera sensor will be used for the detection of signboards at night time.
• The information will be displayed to the driver using HUD(head-up display).

Project Implementation Method

We will implement our project by training the model from datasets of basic traffic signs used and then the image read by the model will be processed and displayed on the screen.

After the acquisition of the dataset, the model is then processed with the machine learning model to achieve good classification performance on real-time data set. After that, it will be fed to the raspberry Pi module which will save the information and display it to the user .

Benefits of the Project

This project will be helping the community in enhancing safety as it allows the driver to concentrate in complicated situations.

It also helps in the reduction of challans.

The comfort of the ride.

Technical Details of Final Deliverable

The project will be delivered in two stages:

Stage1: Data acquisition and testing:

Data acquisition and testing involve the creation of a local dataset. For this, a camera sensor is used to capture all signboard locations in a particular region. This local dataset is used to train the model using sophisticated machine learning models.

Stage 2:implementation of hardware:

Hardware implementation involves the camera being installed on the car for real-time data acquisition. The real-time data is then fed to the Raspberry pi module for model testing.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

IT

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Industry, Innovation and Infrastructure, Sustainable Cities and Communities

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com