The document presents a project to develop a program using a CNN model to detect, classify and recognize traffic signs from live input with 95% accuracy. The methodology involves collecting a dataset, preprocessing it, splitting it, designing and compiling the CNN architecture with appropriate functions and optimizers, training and tuning the model, and deploying it for practical use. The CNN works by processing images through multiple layers and filters to accurately recognize signs. Applications include use in self-driving cars, map services, and automated industries. Challenges include the variety of traffic signs, changes under natural conditions, needing fast and accurate detection and updates, and complexity of sign forms.

1. Presented By :-
Divyanshi Sharma (02696302819)
Ayushi Choudhary (00396307320)
Suraj Suman (01496307320)
Traffic Sign Recognition using
CNN

2. Objective:-
• Creation of a simple running program in Python that helps detect,
classify and recognize a traffic sign from a live input.
• The program will match the image over different filters using a CNN
Model to give an accurate output.

3. Methodology
1.Collect a comprehensive dataset of traffic
sign images.
2. Preprocess the dataset by resizing,
normalizing, and augmenting the images.
3. Split the dataset into training, validation,
and testing sets.
4. Design the architecture of the CNN.
5. Compile the CNN model with the
appropriate loss function and optimizer.
6. the CNN using the training dataset.
7. Tune the hyperparameters of the model
using the validation set.
8. Evaluate the performance of the trained
9. Deploy the model for practical use.

4. The main process behind this is using CNN for proper recognition of signs , as it gives the accurate output , all
while working it across various layers and filters.
Here’s an example of how CNN works for better understanding:-

5. Outcome:
• This project will create a CNN model to identify traffic signs and will
also classify them with approx 95% accuracy.
• we will observe the accuracy and loss changes over a large dataset.

6. Application :
• used in Automatic Driving vehicle as a driving assistance.
• used in map service for effective, fast and accurate traffic data updates.
• Used in Automated Industry for accuracy and assistance.

7. Challenges :
• vast variety of traffic sign
• Susceptibiity to changes under Natural condition
• The whole process of detection and update must be fast and accurate.
• Huge complexity of the forms of traffic sign.