IRJET- Traffic Sign Recognition and Classification using CNN

IJRASET, 2021

With the increasing necessity of autonomous electric vehicles as time passes by, there are a lot of technical prospects within the structure that have a lot of scope for advancement. One such prospect is traffic sign recognition. Several models have already been developed and are in practice but it is evident to everyone within this field that there is still a lot of untapped potential. In this project we implement feature classification using convolutional neural networks to achieve an efficiency and accuracy higher than that of a conventional model. The system first converts the image into grayscale and then three layers of the image are created. By using skilled convolutional neural network which incorporates crucial data of traffic signs and images, they are parallelly assigned to corresponding classes. Results have shown that this system works with great efficiency.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

You've probably heard about self-driving automobiles, in which the passenger can completely rely on the vehicle for transportation. Cars must, however, understand and follow all traffic rules in order to achieve level 5 autonomy. Many researchers and large organisations, including as Tesla, Uber, Google, Mercedes-Benz, Toyota, Ford, Audi, and others, are working on autonomous vehicles and self-driving automobiles in the world of artificial intelligence and technological innovation. As a result, in order for this technology to be accurate, the vehicles must be able to understand traffic signs and make proper decisions. Speed limits, prohibited entry, traffic signals, turn left or right, children crossing, no passing of big trucks, and so on are all examples of traffic signs. Traffic sign classification is the process of determining which class a traffic sign belongs to. In this project, we'll create a deep neural network model that can categorise traffic signals in an image into several groups. Using our model, we can read and understand traffic signs, which is a critical function for all autonomous vehicles. Based on Convolutional Neural Networks, we offer a method for detecting traffic signs (CNN). We employ support vector machines to convert the original image to grey scale, then apply convolutional neural networks with fixed and learnable layers for detection and recognition. The fixed layer can limit the number of interest regions to be detected and crop the boundaries to be as near to the original as possible.

IRJET, 2021

This paper presents an summary of traffic sign detection and recognition. It describes the characteristics and requirements and also difficulties between the road sign identification and recognition of the road signs. It shows the convolutional nueral network technique used for verification and classification of the road signs. The paper introduces a traffic sign detection and recognition system that accurately estimates the situation and exact boundary of traffic signs using convolutional neural network (CNN). during this Python project, we'll build a deep neural network model which will classify traffic signs present within the image into different categories. With this model, we are ready to read and understand traffic signs which are a really important task for all autonomous vehicles.

International Journal of Information Retrieval Research, 2022

In recent times, self-driving vehicles have been widely adopted across different countries as they are equipped to drastically reduce the number of road accidents and congestion on the road thereby improving the traffic efficiency. To detect, identify, and label the traffic signs on the road in order to help the Advanced Driver Assistance Systems (ADAS) in these autonomous vehicles with navigation details, a Traffic Sign Recognition (TSR) System using a deep convolutional neural network model, Mask RCNN (Mask Regional Convolutional Neural Network), is proposed in this paper that aims to help the autonomous vehicles comprehend the road ahead and safely navigate to the desired destination. This paper presents the detection and labelling of Indian and European Signs and also the results of the system working efficiently under various challenging visibility conditions. The results obtained show that the Mask RCNN model has recorded higher performance compared to all the other CNN models...

Advanced Journal of Graduate Research

Machine Learning (ML) involves making a machine able to learn and take decisions on real-life problems by working with an efficient set of algorithms. The generated ML models find application in different areas of research and management. One such field, automotive technology, employs ML enabled commercialized advanced driver assistance systems (ADAS) which include traffic sign recognition as a part. With the increasing demand for the intelligence of vehicles, and the advent of self-driving cars, it is extremely necessary to detect and recognize traffic signs automatically through computer technology. For this, neural networks can be applied for analyzing images of traffic signs for cognitive decision making by autonomous vehicles. Neural networks are the computing systems which act as a means of performing ML. In this work, a convolutional neural network (CNN) based ML model is built for recognition of traffic signs accurately for decision making, when installed in driverless vehic...

IRJET, 2022

This paper aims to summarize the usage of Traffic sign detection and identification and how it could play a massive role in increasing the safety of people while driving. Through this paper, we shall see how a Traffic Sign Recognition system can be implemented using advanced Machine Learning and Convolutional Neural Networks. We believe that implementing such systems could prove beneficial for the evolution of the current safety standards while driving.

IRJET, 2022

Traffic sign acknowledgment framework (TSRS) is a critical part of canny transportation framework (ITS). Having the option to distinguish traffic signs precisely and successfully can work on the driving wellbeing. This paper presents a traffic sign acknowledgment strategy on the strength of profound learning, which mostly focuses on the location and order of roundabout signs. A picture, first and foremost, is pre-processed to feature significant data. Furthermore, Hough Transform is utilized for distinguishing what's more, finding regions. At long last, the distinguished street traffic signs are characterized in view of profound learning. In this article, a traffic sign discovery and distinguishing proof strategy because of the picture handling is proposed, which is joined with convolutional brain organization (CNN) to sort traffic signs. Because of its high acknowledgment rate, CNN can be utilized to acknowledge different PC vision errands. TensorFlow is utilized to carry out CNN. In the German informational collections, we can recognize the roundabout image with over 98.2% precision.

IRJET, 2021

Traffic Sign Detection and Recognition is an important feature for driver assistance, contributing to safety of drivers, pedestrians and vehicles. In order to focus on driving, drivers sometimes miss out traffic signs on road, or due to bad weather conditions (eg. fog, rain etc.) which could be dangerous for drivers as well as pedestrians. Our Software system would help to detect as well as Identify traffic signs without loosing the focus of drivers while driving. To classify image into respective categories, we build a CNN model (convolution Neural Network). CNN is best for image classification purposes. Tensorflow is used to implement CNN. We are able to implement the model with 99% accuracy. Traffic signs are an essential part of our day to day lives. They contain critical information that ensures the safety of all the people around us. Without traffic signs, all the drivers would be clueless about what might be ahead to them and roads can become a mess. The annual global roach crash statistics say that over 3,280 people die every day in a road accident. These numbers would be much higher in case if there were no traffic signs.

WSEAS TRANSACTIONS ON SIGNAL PROCESSING, 2022

Traffic sign is the key aspect in road and also for the autonomous car. Detection and classification of these sign plays a vital role for the invention of driverless vehicles. Convolutional neural network (CNN) has the ability to learn local features using series of convolutional and pooling layer observing the image sequences. In this work, traffic sign detection and classification has been performed based on deep learning approach. The experiment conducted on Germen Traffic Sign Detection Benchmark (GTSDB) and Recognition Benchmark (GTSRB) for detection and recognition. For traffic sign detection a two-stage detector, Faster R-CNN with ResNet 50 backbone structure is used where the CNN layers extracted the features of traffic signs from the images and the region proposal network (RPN) filter the object from the image to create bounding box based on the extracted feature map. The classification network classifies the traffic signs and predict the proposal confidence score. A genera...

IRJET, 2023

For adding the safety of the drivers, pedestrians and vehicles as well, to the driver easement systems, traffic sign recognition feature is required. For developing TSR systems, we need the use of CV (Computer Vision) techniques, which could be viewed as principal in the field of pattern recognition all in all. We are going to use two latest architectures called Lenet-5 model and VGGNet model architectures in two different approaches. In this project, we are going to present the study of two major approaches which are required for developing traffic sign detection and recognition systems. We propose a methodology for traffic sign identification dependent on Convolutional Neural Networks (CNN). First, we are going to transform the original image into greyscale image with the help of SVM(support vector machine) and then use CNN(convolutional neural network) for detecting and recognizing things with fixed and learnable layers we use CNN(convolutional neural network). With fixed layers, we can lessen the measure of interest zones to identify, and trim the limits near the boundaries of traffic signs. The accuracy of detection can be increased with the help of learnable layers. By researching and study of many research papers, we want to give a real-time solution for this challenging problem called TSR (Traffic Sign detection and Recognition).