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Major Project PPT

This document discusses a major project on traffic sign recognition using machine learning. It aims to create an intelligent system that can accurately identify and interpret various traffic signs in real-time to enhance road safety. It explores applying convolutional neural networks to a diverse dataset of traffic sign images to achieve commendable recognition accuracy.

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vadlakondadeepika6
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© © All Rights Reserved
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6 views

Major Project PPT

This document discusses a major project on traffic sign recognition using machine learning. It aims to create an intelligent system that can accurately identify and interpret various traffic signs in real-time to enhance road safety. It explores applying convolutional neural networks to a diverse dataset of traffic sign images to achieve commendable recognition accuracy.

Uploaded by

vadlakondadeepika6
Copyright
© © All Rights Reserved
Available Formats
Download as PDF, TXT or read online on Scribd
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DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

A MAJOR PROJECT
ON
Traffic Sign Recognition

UNDER THE GUIDANCE OF: PRESENTED BY : BATCH NO: A-14

K. Kalyan V. Vineeth Reddy 20271A0550


(Assist. Professor Dept OF CSE) V. Deepika 20271A0508
Md. Sufiyan Ali 20271A0543
M. Laxmi Prasanna 20271A0517
CONTENTS
➢ ABSTRACT

➢INTRODUCTION

➢ PROBLEM STATEMENT

➢ EXISTING SYSTEM

➢ PROPOSED SYSTEM

➢ OBJECTIVES

➢ REQUIREMENTS
ABSTRACT
The ever-increasing demand for intelligent transportation systems has propelled the
need for advanced techniques in traffic sign recognition. This project explores the
application of machine learning algorithms for the automated detection and
classification of traffic signs. Using a diverse dataset comprising various traffic sign
images, we implemented a convolutional neural network (CNN) architecture to
accurately recognize and interpret these signs in real-time scenarios. Through
extensive experimentation and fine-tuning of hyperparameters, our model achieved
a commendable accuracy rate, demonstrating its potential for enhancing road safety
and facilitating efficient traffic management.
INTRODUCTION

➢ Traffic sign recognition serves as a critical component in modern transportation


systems, aiding in the development of autonomous vehicles and enhancing road
safety
➢ By harnessing the power of machine learning, this project aims to create an
intelligent system capable of accurately identifying and interpreting various traffic
signs in real-time.
➢ Leveraging deep learning algorithms and computer vision techniques, we seek to
empower vehicles with the ability to swiftly comprehend and respond to the
diverse array of traffic signs, contributing to a safer and more efficient future of
transportation.
PROBLEM STATEMENT

➢ "Traffic Sign Recognition Using Machine Learning" involves creating an efficient


system that can accurately detect and classify various traffic signs from images or
video streams.
➢ The system should be able to recognize a wide range of traffic signs, including
speed limits, stop signs, yield signs, and more, in different environmental
conditions and varying angles.
➢ Multiclass Classification:
The system needs to distinguish between various traffic signal types, each
with different shapes, colors, and symbols.
➢ Real-time Processing:
Processing speed is crucial for timely decision-making in autonomous
vehicles, requiring low-latency model predictions.
EXISTING SYSTEM:
The system Tam T. Le proposed method concerns blocks of pixels, so it helps to
handle diversification of data. It could be always flawless if an updated traffic sign
location DB would be available. But few cars have GPS installed and traffic sign
localization DB are not available.

PROPOSED SYSTEM:
The earlier computer vision techniques required lots of hard work in data processing
and it took a lot of time to manually extract the features of the image. Now, deep
learning techniques have come to the rescue for recognition of traffic signs in
autonomous vehicles.
OBJECTIVES

Creating a project focused on traffic and recognition using machine learning can be
a complex but rewarding endeavor.
Here are some potential objectives to consider
➢ Anomaly Detection: Build a model that can identify unusual or hazardous
activities on the road, alerting authorities and drivers to potential dangers.
➢ Recognition Systems: Create a robust recognition system for license plates,
allowing for better tracking of vehicles and aiding law enforcement in managing
traffic violations.
➢ Traffic Analysis: Develop a system that can analyze and predict traffic patterns,
helping to optimize routes and reduce congestion.
REQUIRMENTS

➢Building a traffic and recognition project using machine learning involves several
key steps and requirements. Here are the fundamental components you'll need to
consider:
➢Data Collection: a large dataset of labeled images or videos of traffic scenarios to
train your model
➢Model Selection: Choose an appropriate machine learning model, such as
Convolutional Neural Networks (CNNs), for image recognition tasks.
➢Evaluation: Assess the performance of the trained model using metrics like
accuracy, precision, recall, and F1 score.
➢Integration: Integrate the recognition system with appropriate sensors or cameras
for live traffic monitoring.
THANK YOU

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