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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.
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
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.