Deep Learning For Large-Scale Traffic-Sign Detection And Recognition

Project Code :TCPGPY390

Objective

In this project, we propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. 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.

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 recognize 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)

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