Traffic Sign Classification using Deep Learning

IEEE BASE PAPER ABSTRACT:

The ability of a vehicle to self-drive is receiving a lot of attention these days. One of the most significant features of a self-driving car is its capacity to identify traffic signs, which ensures the safety and security of passengers both inside and outside the vehicle. Negligence in viewing and interpreting traffic signs accurately is a major cause of road accidents. A system that assists in identifying traffic signs, allowing the vehicle to make appropriate judgments is essential. Therefore, a method based on a Convolutional Neural Network (CNN), which aids in the classification of traffic signs has been suggested in this paper. The main objective is to develop a system with better performance that helps in the classification of the signs efficiently to avoid accidents and encourage the development of autonomous vehicles. Moreover, the proposed model is compared with the standard Le-Net 5 model. The accuracy for the proposed TrafficSign CNN model is 95.8%.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

MobileNet Architecture & YOLOv5.

OUR PROPOSED PROJECT ABSTRACT:

The project “Traffic Sign Classification using Deep Learning” represents a significant advancement in the field of computer vision, specifically focusing on the recognition and classification of traffic signs. Leveraging the power of Python, two distinctive models were employed to address the complex challenges associated with traffic sign classification: MobileNet Architecture and YOLOv5.

With MobileNet Architecture, an impressive level of performance was achieved, with a Training Accuracy of 97.00% and Validation Accuracy of 98.00%. This achievement was realized through the utilization of a meticulously curated dataset comprising 4,170 images encompassing a diverse array of 58 traffic sign classes, including but not limited to speed limits, directional instructions, prohibitory signs, and hazard warnings. These classes span the entire spectrum of traffic regulation, ensuring comprehensive coverage of the subject matter.

Moving forward, the implementation of YOLOv5 introduced real-time traffic sign recognition using image data and real time web camera data. This model was trained on a dataset comprising 39 unique traffic sign classes. These classes encompass a wide range of signs, such as pedestrian crossings, speed limits, warnings, and regulatory signs, contributing to the project’s practical applicability in real-world scenarios.

The project represents a notable contribution to the field of deep learning-based traffic sign classification. By employing two distinct architectures, it ensures both high accuracy and real-time capability, addressing the growing demand for intelligent traffic sign recognition systems. The results showcase the feasibility of employing deep learning techniques to enhance road safety and traffic management.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED.
Input Devices : Keyboard, Mouse.
Ram : 8 GB.

SOFTWARE REQUIREMENTS:

Operating system : Windows 10 / 11.
Coding Language : Python 3.10.9.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Ramya Sree Pothineni, Srinivas Inampudi, Lakshmi Yesaswini Gudavalli, T. Lakshmi Surekha, “Traffic Sign Classification using Deep Learning”, 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), IEEE CONFERENCE, 2023.

Python IEEE Projects

Traffic Sign Classification using Deep Learning