FEATURE EXTRACTION AND RECOGNITION ON TRAFFIC SIGN IMAGES

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.

3C Tecnología_Glosas de innovación aplicadas a la pyme, 2020

TSR (Traffic Sign Recognition) represents an important feature of advanced driver assistance system, contributing to the safety of the drivers, autonomous vehicles as well and to increase driving comfort. In today's world road conditions drastically improved as compared with past decades. Obviously, vehicle's speed increased. So, on driver's point of view there might be chances of neglecting mandatory road signs while driving. This paper explores the system to helps the driver about recognition of road signs to avoid road accidents. TSR is challenging task, while its accuracy depends on two aspects: feature extractor and classifier. Current popular algorithms mainly deploy CNN (Convolutional Neural Network) to execute both feature extraction and classification. In this paper, we implement the traffic sign recognition by using CNN, the CNN will be trained by using the dataset of 43 different classes of traffic signs along with TensorFlow library. The results will show the 95% accuracy.

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...

IEEE, 2023

The recognition and classification of traffic signs hold significant significance within intelligent transportation systems and autonomous vehicles. This task entails the precise and real-time identification and categorization of diverse traffic signs through the There are many different types of neural networks that can be used for traffic sign recognition and classification, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). The choice of which network to use depends on the specific requirements and constraints of the task. The field continues to evolve and advance, with new techniques and approaches being developed to address the challenges and limitations of this task [1].

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021

This paper presents an effective solution to detecting traffic signs on road by first classifying the traffic sign images us-ing Convolutional Neural Network (CNN) on the German Traffic Sign Recognition Benchmark (GTSRB)[1] and then detecting the images of Indian Traffic Signs using the Indian Dataset which will be used as testing dataset while building classification model. Therefore this system helps electric cars or self driving cars to recognise the traffic signs efficiently and correctly. The system involves two parts, detection of traffic signs from the environment and classification based on CNN thereby recognising the traffic sign. The classification involves building a CNN model of different filters of dimensions 3 × 3, 5 × 5, 9 × 9, 13 × 13, 15 × 15,19 × 19, 23 × 23, 25 × 25 and 31 ×31 from which the most efficient filter is chosen for further classifying the image detected. The detection involves detecting the traffic sign using YOLO v3-v4 and BLOB detection. Transfer Learning is used for using the trained model for detecting Indian traffic sign images.

2022

Autonomous vehicles are one of the increasingly widespread application areas in automotive technology. These vehicles show significant potential in improving transportation systems, with their ability to communicate, coordinate and drive autonomously. These vehicles, which move from source to destination without human intervention, appear to be a solution to various problems caused by people in traffic, such as accidents and traffic jams. Traffic accidents and traffic jams are largely due to driver faults and non-compliance with traffic rules. For this reason, it is predicted that integrating artificial intelligence (AI)-based systems into autonomous vehicles will be a solution to such situations, which are seen as a problem in social life. Looking at the literature, VGGNet, ResNet50, MobileNetV2, NASNetMobile, Feed Forward Neural Networks, Recurrent Neural Networks, Long-Short Term Memory, and Gate Recurrent Units It is seen that deep learning models such as these are widely used in traffic sign classification studies. Unlike previous studies, in this study, a deep learning application was made for the detection of traffic signs and markers using an open-source data set and models of YOLOv5 versions. The original data set was prepared and used in the study. Labeling of this data set in accordance with different AI models has been completed. In the developed CNN models, the training process of the data set containing 15 different traffic sign classes was carried out. The results of these models were systematically compared, and optimum performance values were obtained from the models with hyper parameter changes. Real-time application was made using the YOLOv5s model. As a result, a success rate of 98-99% was achieved.

2021

Traffic signs are mandatory features of road traffic regulations worldwide. Automatic detection and recognition of traffic signs by vehicles may increase the safety level of drivers and passengers. For this reason, Real Time-Traffic Sign Recognition (RT-TSR) system is one of the essential components for smart transportation systems and high-tech vehicles. Recently, very good performances have been achieved in public datasets, especially with advanced Computer Vision (CV) approaches like Deep Learning (DL). Nevertheless, these CV techniques still need to be improved to provide the requirements of Real-Time (RT) applications. Although hopefully outcomes have been obtained theoretically in previous Traffic Sign Recognition (TSR) studies, there are very few studies that offer RT solutions in the real world. Therefore, in this study, a DL-based RT-TSR system is developed because of its high rate of recognition and quick execution. Besides, the CV approach has been included in the softwar...

IOS Press eBooks, 2022

Automatic Traffic Sign Detection and Recognition (ATDR) system has been expanded and implemented partly in Intelligent Transportation System (ITS) that is actively used today. As traffic congestion increases, the manufacturing industry may have made and installed the ATDR systems in various types of vehicles, including cars, light commercial vehicles, and heavy trucks that act as driver assistance systems. ATDR system not only helps to minimize the number of traffic accidents but also supports road users through legal and compliant guidance and providing all traffic information, so that road users can be more attentive and solve them immediately in a short duration of time. On the point of the safety of the road users and others will be protected throughout a critical situation with identify the driving scene. In this paper, a deep-architectural neural networks, which is the convolutional neural network (CNN/ConvNet) model are chosen to expand in the ATDR system. With its excellent ability to train, research, and organize data, CNN is becoming one of the most widely used machine learning algorithms in key tasks such as prediction and classification. First, various open source of deep learning libraries will be studied. The library has been tested using the Malaysian Traffic Sign (MTS) dataset, which contains 32891 labelled images with real-world signs. Then the combination of both designed detection model and the CNN classification model will be trained under some parameter settings. Finally, the proposed system will detect and recognize the different types of traffic sign images in real-time display and Graphical User Interface (GUI). The achievable test accuracy is exceeded by 98% on a total of 43 different classes of MTS data that is obtained and for all study cases.

Intelligent Decision Technologies, 2019

In this paper, we have proposed and developed a comprehensive Convolutional Neural Network (CNN) classifier "WAF-LeNet" to be used in traffic signs recognition and identification as an empowerment of autonomous driving technologies. The implemented architecture is a deep fifteen-layer network that has been selected after extensive trials to be fast enough to suit the designated application. The CNN got trained using Adam's optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. The learning process is carried out using the well-known "German Traffic Sign Dataset-GTSRB". The data has been partitioned into training, validation and testing data sets. Additionally, more random traffic signs images are collected from the web and further used to test the robustness of the proposed CNN classifier. The paper goes through the development process in details and shows the image processing pipeline harnessed in the development. The proposed approach proved successful in identifying correctly 96.5% of the testing data set and 100% of the robustness dataset with the much smaller and faster network than other counterparts.

International Journal of Engineering Applied Sciences and Technology, 2020

Convolutional Neural Networks mostly use deep learning algorithms to detect and identify traffic signs till now but they are lacking in so many ways. This paper will give a really effective method for traffic sign detection and identification using convolutional neural networks. Convolutional Neural Networks are used for road sign detection and classification as it takes an input image and then assigns weights to different aspects in the image and then differentiate them from each other. Other classification algorithms require much longer preprocessing than the ConvNet. The filters which are there in primitive methods are engineered manually with training. These filters are learned by the ConvNets. Neurons respond to stimuli in the receptive field only, which is a restricted region of the visual field. The temporal and spatial dependencies of an image can be successfully captured by applying the relevant filters. To understand the sophistication of an image in a better way, the network can be trained. After reducing the parameters and weights reusability, the architecture fits better with the image dataset. The architecture of the system is designed in such a way that it extracts important features from the traffic sign's images and classifies them under various categories.