CNN Design for Traffic Signs Recognition and Translation

2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016

Recognition of traffic signs is vary important in many applications such as in self-driving car/driverless car, traffic mapping and traffic surveillance. Recently, deep learning models demonstrated prominent representation capacity, and achieved outstanding performance in traffic sign recognition. In this paper, we propose a traffic sign recognition system by applying convolutional neural network (CNN). In comparison with previous methods which usually use CNN as feature extractor and multilayer perception (MLP) as classifier, we proposed max pooling positions (MPPs) as an effective discriminative feature to predict category labels. Through extensive experiments, MPPs demonstrates the ideal characteristics of small inter-class variance and large intra-class variance. Moreover, with the German Traffic Sign Recognition Benchmark (GTSRB), outstanding performance has been achieved by using MPPs.

Scientific Journal of Astana IT University

Recognizing road signs is one of the most important steps drivers can take to help prevent accidents. The purpose of the research work is to develop a recognition system, increasing the classification accuracy of the model, using deep learning methods of the road sign recognition system for drivers in real time on the road. Stages of road sign image classification were carried out, and other authors' solutions were analyzed. In addition, in this work, a convolutional neural network (CNN) was used for an autonomous traffic and road sign detection and recognition system. The proposed system works in real-time on the recognition of road signs images. In this paper, a model is trained using deep learning of 43 different road signs using existing datasets and collected local road signs. A traffic sign detection and recognition system is presented using an 8-layer convolutional neural network, which acquires different functions by training different types of traffic signs. In previous...

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.

2016

In this work, a new library for training deep neural networks for image classification was implemented from the ground up, with the purpose of supporting GPU acceleration through OpenCL™, an open framework for heterogeneous parallel computing. The library introduced here is the first attempt at creating a C# deep learning toolbox, and can thus be more easily integrated with other projects under the .NET framework. The availability of cross-platform tools, covering as many developing environments as possible, can in fact accelerate the deployment of deep learning algorithms into a wide range of industrial applications, including advanced driver assistance systems and autonomous vehicles. The library was tested on the German Traffic Sign Recognition Benchmark (GTSRB) data set, containing 51839 labelled images of real-world traffic signs. The performance of a classic deep convolutional architecture (LeNet) was compared to that of a deeper one (VGGNet), when trained with different regul...

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...

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.

Artificial Neural Networks enables solving many problems in which classical computing is not up to task. Neural Networks and Deep Learning currently provide the best solutions to problems in image recognition, speech recognition and natural language processing. In this paper a Neural Network, more specific-Convolutional Neural Network solution for the purpose of recognizing and classifying road traffic signs is proposed. Such solution could be used in autonomous vehicle production, and also similar solutions could easily be implemented in any other application that requires image object recognition.

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.

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...

International Journal of Computer Science and Information Security (IJCSIS), Vol. 22, No. 3, June 2024, 2024

This article focuses on Convolutional Neural Networks (CNNs) that can help recognize traffic signs for automated driving and their importance in improving traffic safety. This study involves training a CNN model that will be used in identifying and categorizing traffic signs to ensure effective communication between the road environment, as well as driver-operated and autonomous vehicles. The efficiency of advanced threading techniques for data processing and the models’ effectiveness is also investigated in the study. This study revealed a great prospect that is hidden in CNN traffic sign recognition, thus contributing to the development of advanced autonomous driving systems. It is concluded that these model prototypes are ready for redesign to improve on them considering the dynamic nature of traffic conditions and recent developments in vehicle automation technologies.