Traffic Sign Recognition System using CNN

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Nevon Driver Drowsiness Detection System Using Python
nevon software

Traffic sign detection and recognition have gained importance with advances in image processing due to the benefits that such a system may provide. The recent developments and interest in self-driving cars have also increased the interest in this field.

Automatic detection and recognition of traffic signs is very important and could potentially be used for driver assistance to reduce accidents and eventually in driverless automobiles. There are many sign boards on roads that people are unaware of. They might have seen traffic or road signs all the time but don’t understand what those signs are indicating. Our Traffic Sign Recognition System detects and recognizes road signs or traffic signs from an image and video. It will also recognize signs in real-time using CNN. Here, Deep Convolutional Neural Network (CNN) is used to develop Autonomous Traffic or Road Sign detection.

In this system, the user can upload an image for the system to detect and recognize traffic signs. The system can recognize uploaded videos. For real-time traffic sign recognition, the user will need to turn on their device camera and click on the camera button to start detecting.

The system will display the names of the recognized traffic signs. It will also display the probability of the predicted result being accurate. The front-end involves Html, CSS, and JavaScript and the back-end involves Python. The framework used is Django and the database is MySQL.

Here, we have implemented the Convolutional Neural Networks (CNN) model. The Traffic Sign dataset is extracted from Kaggle. We can implement this system on a vehicle from a broader perspective and modify the vehicle by this system to behave as a sign detector in real-time.


  • It is easy to maintain.
  • It is user-friendly.
  • The system automatically detects traffic signs.
  • It can recognize signs from an image and videos.
  • It can also detect and recognize in real-time.