TRAFFIC SIGN CLASSIFIER🚦

Introduction

You must have heard about the self-driving cars in which the passenger can fully depend on the car for traveling. But to achieve level 5 autonomous, it is necessary for vehicles to understand and follow all traffic rules.

In the world of Artificial Intelligence and advancement in technologies, many researchers and big companies like Tesla, Uber, Google, Mercedes-Benz, Toyota, Ford, Audi, etc are working on autonomous vehicles and self-driving cars. So, for achieving accuracy in this technology, the vehicles should be able to interpret traffic signs and make decisions accordingly.

What is Traffic Sign Recognition ❓

There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to.

About the Project

In this Python project example, we will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.

Dataset of Python Project

For this project, we are using the public dataset available at Kaggle:

GTSRB - German Traffic Sign Recognition Benchmark

The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties:

• Single-image, multi-class classification problem.
• More than 40 classes.
• More than 50,000 images in total.
• Large, lifelike database.

Usage

• Open CMD in working directory.
• Run Traffic_classifier.py
• Go to the website http://127.0.0.1:5000/ and test it.

Screenshots

Conclusion

Traffic sign classification is a critical component of autonomous driving. With the help of CNNs and largedatasets, we have made significant progress in this field. However, there are still challenges to overcome. Future research should focus on improving the robustness of classifiers and addressing the limitations of current approaches.