Overview of Deep Learning Technique for Road Traffic Sign Detection and Recognition
TL;DR
This article is analysis the process of convolutional neural networks (CNNs) and other approaches, underlining their assets, limitations, and applicability to real-time scenarios, and the compare analysis of traffic sign recognition systems.
Abstract
In India, it is a country that has a well-known and dense road network. On these roads, the traffic signals benefit the driver in delightful the driving conclusions appropriately. These traffic signals effort as the noiseless helper to the driver. These indicators tell approximately the complaint of the road forward. So that the driver can take the improved and effective conclusion nearby how to drive and what are the influences to take care of. Normally, these signs are used to presage the driver nearby the possessions which are constrained or which can help the driver to sidestep the coincidence. Occasionally these indicators are correspondingly rummage-sale to border the driver to go overhead the limitation restricted on that road, like the rapidity limit. Every driver on road must be accustomed with all these signs and this is confirmed while philanthropic driving license to any driver. In this article, describe about the technique for road traffic sign detection and recognition. It is analysis the process of convolutional neural networks (CNNs) and other approaches, underlining their assets, limitations, and applicability to real-time scenarios. the article also discusses about the compare analysis of traffic sign recognition systems. Keywords— Road Traffic Sign Detection, Traffic Sign Recognition (TSR), Deep Learning, Convolutional Neural Networks (CNN), Object Detection, YOLO, Faster R-CNN, Single Shot Detector (SSD).