Review. Machine learning techniques for traffic sign detection

12/12/2017
by   Rinat Mukhometzianov, et al.
0

An automatic road sign detection system localizes road signs from within images captured by an on-board camera of a vehicle, and support the driver to properly ride the vehicle. Most existing algorithms include a preprocessing step, feature extraction and detection step. This paper arranges the methods applied to road sign detection into two groups: general machine learning, neural networks. In this review, the issues related to automatic road sign detection are addressed, the popular existing methods developed to tackle the road sign detection problem are reviewed, and a comparison of the features of these methods is composed.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/20/2014

Optimized Method for Iranian Road Signs Detection and recognition system

Road sign recognition is one of the core technologies in Intelligent Tra...
research
10/01/2013

Multiclass Road Sign Detection using Multiplicative Kernel

We consider the problem of multiclass road sign detection using a classi...
research
10/05/2021

Traffic control Management System and Collision Avoidance System

Many road accidents occur due to drivers failing to read sign board due ...
research
10/19/2010

Joint interpretation of on-board vision and static GPS cartography for determination of correct speed limit

We present here a first prototype of a "Speed Limit Support" Advance Dri...
research
12/04/2020

Accelerating Road Sign Ground Truth Construction with Knowledge Graph and Machine Learning

Having a comprehensive, high-quality dataset of road sign annotation is ...
research
10/13/2020

Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture

With the large uses of the intelligent systems in different domains, and...
research
09/08/2021

RoadAtlas: Intelligent Platform for Automated Road Defect Detection and Asset Management

With the rapid development of intelligent detection algorithms based on ...

Please sign up or login with your details

Forgot password? Click here to reset