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PROJECT


Build a Road Sign Recognition System with CNN

In this project, we will create road sign classification using convolutional neural networks (CNN). This project will address a real-world problem with significant implications for road safety. Road signs play a crucial role in traffic management, providing important instructions and warnings to drivers.

Build a Road Sign Recognition System with CNN

You will learn to:

Visualize a large image dataset.

Preprocess the data.

Augment data using data augmentation techniques.

Create and understand the confusion matrix.

Build a CNN for road-sign recognition.

Skills

Computer Vision

Image Visualisation

Deep Learning

Prerequisites

Understanding of machine learning fundamentals

Basic Python programming

Image processing basics

Data handling skills

Basic understanding of CNNs

Technologies

Numpy

Python

OpenCV

TensorFlow

Project Description

The goal of this project is to train a model to recognize and classify different road signs from images. This will help improve road safety by automating the identification of road signs, a crucial aspect of autonomous vehicles and traffic management systems.

The project commences with a meticulous data collection phase, sourcing a diverse dataset encompassing various types of road signs. These signs are obtained according to different lighting conditions, perspectives, and environmental settings to ensure the model’s robustness and adaptability.

The core technological foundation of this project revolves around CNNs, a specialized deep learning architecture designed specifically for image recognition tasks. The development of this road sign classification system, leveraging Python-based libraries such as TensorFlow and utilizing CNNs, represents a pivotal step toward enhancing road safety and advancing the capabilities of autonomous vehicles and traffic management systems.

Project Tasks

1

Load and Process the Dataset

Task 0: Get Started

Task 1: Import Modules and Dependencies

Task 2: Load the Dataset

Task 3: Get the Names and Numbers of Classes

Task 4: Visualize the Dataset

Task 5: Split the Dataset

2

Data Preprocessing

Task 6: Declare the Constants

Task 7: Shuffle and Prefetch the Data

Task 8: Create the Resizing and Rescaling Layer

Task 9: Carry out Data Augmentation

Task 10: Implement Data Augmentation

3

Build the Convolution Neural Network

Task 11: Define the Parameters for Model Building

Task 12: Build the CNN’s Architecture

4

Train the Convolution Neural Network

Task 13: Compile the Model

Task 14: Train the Model

Task 15: Evaluate the Model

5

Accuracy and Loss Curves

Task 16: Define the Training and Validation Metrics

Task 17: Plot Accuracy and Loss Curves

6

Test the Convolution Neural Network

Task 18: Script a Predict Function

Task 19: Test the Model on a Single Image

Task 20: Test the Model on Multiple Images

Congratulations