Voice based Traffic Sign by using Deep Learning

Project Code :TCMAPY967

Objective

The main objective of the Voice Based Traffic Sign system using Deep Learning is to develop an intelligent and accessible solution that uses voice commands to interpret and communicate real-time traffic signs, enhancing road safety and providing assistance to individuals with visual impairments or limited visibility. By leveraging deep learning algorithms, this system aims to accurately recognize and convert traffic sign information into audible cues, enabling users to navigate and interact with traffic signs effectively.

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. In our proposed method we are using Convolution Neural Network (CNN) which can detect and recognise the traffic signs. This approach is applied to detection of 43 traffic sign categories. Once after the training with CNN we can check for the results.

Keywords: Traffic sign detection and recognition, Deep Learning, Convolution Neural Network (CNN), Camera.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.