to download project base paper

to download project abstract

ABSTRACT
Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community, the recognition and detection of traffic signs are a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced driver-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs (around 50 categories out of several hundred) and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 200 traffic-sign categories represented in our novel dataset. The results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with a large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of the traffic-sign inventory management.

Advanced Computer Vision Model for Aiding Automobiles in Traffic Sign Classification

Abstract:

The “Advanced Computer Vision Model for Aiding Automobiles in Traffic Sign Classification” project addresses the critical need for robust and efficient traffic sign recognition systems in autonomous vehicles. Leveraging advanced computer vision techniques and web technologies, this project introduces an intelligent model capable of accurately classifying and interpreting traffic signs in real-time scenarios.

Problem Statement:

Autonomous vehicles heavily rely on accurate and real-time interpretation of traffic signs for safe navigation. Existing traffic sign recognition systems may face challenges in handling complex scenarios, adverse weather conditions, or varied sign placements. This project aims to enhance the capabilities of traffic sign classification models to ensure reliable and precise recognition in diverse conditions.

Motivation:

The motivation behind this project is to contribute to the development of safer and more reliable autonomous transportation. By improving the accuracy and efficiency of traffic sign classification, this project seeks to enhance the overall performance and safety of autonomous vehicles, addressing critical concerns related to road safety and traffic management.

Existing System:

Current traffic sign recognition systems in autonomous vehicles may rely on traditional computer vision approaches, which may struggle in complex scenarios, low-light conditions, or with obscured signs. There is a need for a more advanced system capable of handling a diverse range of real-world challenges.

Advanced computer vision model for aiding automobiles in traffic sign classification
Advanced computer vision model for aiding automobiles in traffic sign classification

Proposed System:

The proposed system introduces an advanced computer vision model, possibly leveraging deep learning architectures like Convolutional Neural Networks (CNNs) or other state-of-the-art models. This system aims to provide accurate and real-time traffic sign classification, enabling autonomous vehicles to make informed decisions based on the interpretation of road signs.

Modules Explanation:

  1. Data Collection and Preprocessing:
  • Gather diverse datasets containing images of traffic signs and preprocess the data for model training.
  1. Deep Learning Model Training:
  • Train a deep learning model, such as a CNN, using the preprocessed data for traffic sign classification.
  1. Real-time Traffic Sign Detection:
  • Implement real-time detection and classification of traffic signs using the trained model.
  1. Integration with Automobile Systems:
  • Integrate the traffic sign classification system with the existing automobile control systems for informed decision-making.

System Requirements:

  1. Hardware:
  • High-performance GPUs for training deep learning models.
  • Cameras and sensors for real-time image capture in automobiles.
  1. Software:
  • Python for model development.
  • Deep learning frameworks (TensorFlow or PyTorch).
  • Web development frameworks (Django or Flask).

Algorithms:

  1. Deep Learning Model (e.g., CNN):
  • Utilize a deep learning model for traffic sign classification, trained on diverse datasets.

Architecture:

The system architecture involves the integration of the deep learning model into the existing automobile systems, allowing for real-time traffic sign detection and classification.

Technologies Used:

  1. Programming Languages:
  • Python for backend development.
  • HTML, CSS, JavaScript for frontend development.
  1. Deep Learning Framework:
  • TensorFlow or PyTorch for implementing and training the deep learning model.
  1. Web Framework:
  • Django or Flask for building the web application interface.

Web User Interface:

The web interface provides a user-friendly platform for monitoring and evaluating the real-time traffic sign classification results. It allows users to view the system’s interpretation of detected signs and their corresponding actions.

This project strives to contribute to the advancement of autonomous vehicles by developing a sophisticated traffic sign classification system, enhancing safety, and ensuring the reliable interpretation of road signs in diverse conditions.

UML DIAGRAMS

Collaboration Diagram

Collaboration Diagram

Architecture diagram

Architecture diagram

class diagram

class diagram

sequence diagram

sequence diagram

use case diagram

use case diagram

activity diagram

activity diagram

component diagram

component diagram

Deployment Diagram

Deployment Diagram

Flow chart Diagram

Flow chart Diagram

Leave a Comment

Comments

No comments yet. Why don’t you start the discussion?

Leave a Reply

Your email address will not be published. Required fields are marked *