English

An automatic road sign recognition system based on a computational model of human recognition processing

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies road signs assisting the driver of the vehicle to properly operate the vehicle. This paper presents an automatic road sign recognition system capable of analysing live images, detecting multiple road signs within images, and classifying the type of the detected road signs. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space and locates road signs. The classification module determines the type of detected road signs using a series of one to one architectural Multi Layer Perceptron neural networks. The performances of the classifiers that are trained using Resillient Backpropagation and Scaled Conjugate Gradient algorithms are compared. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 96% using Scaled Conjugate Gradient trained classifiers.

This research paper examines the application of artificial intelligence (AI) in road network inventory and network-wide road safety assessment. An overview of current research related to this field is presented and various aspects of the use of AI in road infrastructure and safety assessment are analysed. In today’s world, where the road network continues to develop and grow, the use of AI can offer significant advantages in the inventory and assessment of road infrastructure and safety. AI can be used to process large volumes of data collected from various sources such as geographic data, photos, documentation and more. One of the main aspects that the article looks at is the use of AI to automatically process data and create a digital inventory of the road network. AI systems can analyse geographic data and photos to identify and classify roads, road signs, markings, and other elements of road infrastructure. This can facilitate engineers and competent authorities in the process of road inventory and maintenance. Another important aspect discussed in the paper is the use of AI for the general assessment of road safety. AI models can analyse road infrastructure inventory data, as well as traffic data, crashes, and other factors, to provide a comprehensive assessment of road network safety. This allows the competent authorities to identify vulnerable places, carry out preventive measures and plan appropriate interventions aimed at improving road safety. In conclusion, the paper highlights the possibilities and advantages of using artificial intelligence in road network inventory and network-wide road safety assessment. Such an approach can contribute to more efficient management of road infrastructure and improvement of road safety.

Background: Automotive headlamps mostly use the tungsten halogen bulb but several years ago a new type of headlamp, the gas discharge bulb, was introduced. Because of the different spectral output of this type of lamp, there has been a suggestion that it may affect the colour recognition and sign conspicuity under night‐time conditions. In this study, the visibility of the road signs is used to examine the effect of the gas discharge lamp's spectrum compared with that of the conventional halogen headlamp.Methods: The spectral output of the lamps and the spectral reflectance of common‐coloured road signs were measured using a Spectra‐Pritchard spectroradiometer. Using luminous reflectance data, chromaticity co‐ordinates and the colorimetric shift of the road signs, when illuminated by gas discharge lamps, were plotted using CIE x, y co‐ordinate system. Colour rendering indices of the lamp were calculated using Munsell samples and road signs as proscribed by the CIE Publication. In addition, the visibility index of the road signs was calculated using Adrian's ‘Visibility of Target’ model.Results: The gas discharge headlamp has more energy in the blue region and less energy in the red region of the spectrum than the halogen headlamp. The general colour rendering index of the gas discharge lamp is higher than that of the halogen lamp. When compared with daylight, all coloured road signs used in this study have less colorimetric shift when illuminated by the gas discharge headlamp than by the halogen headlamp.Conclusion: The result indicates that the gas discharge lamp, while having a very different spectrum from daylight or tungsten halogen lamps, should not have a deleterious effect on sign detection or recognition, when compared to daylight or tungsten halogen lamps.

A vision-based driver assistance system using image processing technology consists of three main modules: road detection, traffic sign detection/identification, obstacle detection. Traffic Sign Recognition is an upcoming research field under applied Computer Vision and Machine Learning which deals with the automatic detection and classification of traffic signs in traffic scene images. In the current paper, we discuss the methodology used to detect the road sign from a traffic sign scene image and present a technique that can be used to construct a system that recognizes road signs in images. Such a system will ensure that each driver is aware of the rules & hazards on the road & will thus help in improving road safety. Also, the location of the road sign in the traffic sign image is unknown. Once these obstacles are overcome, such a system could be integrated in a Driver Assistance System. The primary objective is to develop an algorithm, which will identify various types of road signs from streaming video of real time road scenes in a reasonable time frame. The algorithm has two main parts: the first one, detection which uses the color and shape information to detect a road sign in a traffic scene picture. The second one, prediction & classification based on Neural Network concept. Our algorithm proposes a variety of MATLAB Image Processing Toolbox commands to determine if a road sign is present in current image. If present, the sign is resized and certain features from the Region of Interest (RoI) are fed as a feature vector to a neural net. This trained Neural Classifier then predicts the class of the road sign. Genetic Algorithms (GA) with built-in intelligence could be incorporated into the current system which will try to look for traffic signs only in environments where there is probability of finding/detecting one.

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology.

This paper explores the effective approach of road sign detection and recognition for Driver Assistance Systems (DAS). In today's world road conditions drastically improved as compared with past decade. Express highways equipped with increased lane size made up with cement concrete. Obviously speed of the vehicle increased. So on driver point of view there might be chances of neglecting mandatory road sign while driving. This paper illustrates proposed system to help driver about the road sign detection to avoid road accidents. The automatic road-signs recognition is an important part of Driver Assisting Systems which helps driver to increase safety and driving comfort. In this paper an efficient approach for the detection and recognition of the road sign in the road and acquiring the traffic scene images from a moving vehicle is present. In this paper the road sign recognition system is to be divided into two parts, the first part is detection stage which is used to detect the signs from a whole image, and the second part is classification stage that classifies the detected sign in the first part into one of the reference signs which are presents in the dataset. In the detection module segments, the input image in a YCBCR colour space, and then it detects road signs by using the shape filtering method. The classification module present determines the type of detected road signs by using an artificial neural network (ANN). The extensive experimentation has shown that the proposed system approach is robust enough to detect and the recognize road signs under varying lighting, rotation and translation conditions.

The paper presents a system for the detection and recognition of road signs in real time (tested for Indian road signs). The detection and recognition algorithm used is invariant to scale, angle, blur extent, and variation in lighting condition. Shape classification of road signs using Hu moments is done in order to categorize signs as either warning, mandatory, prohibitory or informational. Classified road signs are then matched to ideal road signs using feature extraction, and the matching is done with the help of Oriented FAST and Rotated BRIEF (ORB) descriptors. After recognition, the driver is given a feedback.

This article is dedicated to analyzing advanced approaches in temporal modeling and real-time gesture recognition within sign language recognition (SLR) systems. Sign glosses are expressed through the spatio-temporal characteristics of visual information, which requires the use of sequence-processing models for their automatic recognition. The study primarily evaluates the effectiveness of three key models: Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCN), and Transformer-based architectures. The article also examines methods applied for real-time analysis of sign glosses, including: Sliding window segmentation of video streams; Self-attention mechanisms for identifying dependencies between gestures; Gloss mapping algorithms for linking sign movements to linguistic units; Ontological integration techniques for enhancing semantic accuracy. Practical results indicate that combining temporal modeling with semantic analysis and contextual verification algorithms ensures continuous and high-accuracy recognition of sign movements. In particular, multimodal systems (video + sensor + gloss) utilizing Transformer-based approaches achieved superior performance in real-time conversion of continuous sign gloss streams into text. The findings of this study hold practical significance for the development of smart assistive devices for automatic sign language translation, interactive interfaces for hearing-impaired users, and specialized SLR platforms for educational and instructional purposes.

ABSTRACTSign language is a communication tool for deaf and dumb people that includes known signs or body gestures to transfer meanings. It uses shapes, directions, movements of hands, and also facial expressions. A sign not only transmits a word but also conveys a tone. Many of the deaf people are not only able to speak, but also not able to write or read a language, so developing sign language translation or in other words sign language recognition (SLR) system can be very vital in their life. The SLR is extremely desired because of its capability to overcome the obstacles between deaf and normal people. It is one of the most important research fields in the human computer interaction studies. Hence, this paper presents an overview of the recent main research works with the vision-based SLR system, and the existing recognition techniques are discussed. Next, we focus on video-based SLR system and perform continuous SLR within video sequences.

Traffic Sign Recognition (TSR) system is a vital component of intelligent transport system. It plays an important role by enhancing the safety of the drivers, pedestrians and vehicles as traffic signs provide important information of the traffic environment of the road and assist the drivers to drive more safely and easily by guiding and warning. This paper represents road sign detection and recognition system based on speeded up robust feature (SURF) descriptor that is invariant to rotation, skew and occlusion of the sign and artificial neural network (ANN) classifier. The proposed system consists of three steps. In the first step the road scene image is segmented using color based thresholding algorithm followed by some post processing like morphological analysis and filtering using region properties to extract the road sign as a blob of region. Next, shape verification is carried out by using translation, scaling and rotation invariant features vector named Distance to Borders (DtBs) of the segmented blob. Finally the recognition of the traffic sign is implemented using ANN classifier with the training of SURF descriptor. The proposed system simulated on offline road scene images shows a high success rate in the recognition stage. The performance of the classifier is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curve.

This descriptive study aims to assess the knowledge and awareness of road safety among university students. The study was conducted among students in Universiti Tun Hussein Onn Malaysia by means of questionnaire disseminated online via social media with shareable link to a Google form. The respondents were self-selected to participate in this study where their responses were self-administrated. Questionnaire consisted of 3 sections included demographic information, knowledge on road signs and road safety law, and road safety awareness. 371 students participated in this study, 66% of them age 23 to 27 years old, 61% were female, 92.5% of respondents have at least one type of license with majority agreed that occurrence of accidents resulted in an increase in road safety awareness. The study found that more than half of the participants could not recognise road sign like parking totally prohibited and speed limit ends here. While, 38% of them correctly identified posted speed limit for expressway. Overall, participants have fair understanding on road safety. Therefore, road safety programmes and education are still relevant to university students as young drivers on the road which is important to increase safety awareness.

This paper addresses an important issue — automatic road traffic signs detection and recognition in natural scenes, which is accomplished in the three following stages: interest points detection, clustering of those points and similarity search. At the first stage, good discriminative, rotation and scale invariant interest points are selected from the image edges based on the 1-D empirical mode decomposition (EMD). Then stable local features related to the brightness and color are extracted using Gabor filter and the detected points are clustered to find the possible candidate road signs or the region of interests (ROIs). We propose a two-step unsupervised clustering technique, which is adaptive. Finally, a fringe-adjusted joint transform correlation (JTC) technique is used for matching the unknown signs with the existing known reference road signs in the database. The presented framework provides a novel way to retrieve a road sign from the natural scenes.

The purpose of this work is to find methods to improve the life quality of city residents and guests by improving mobility conditions. According to expert estimates, the total damage due to problems of urban transport systems is 6–8 % of Russia's gross domestic product. Mortality rates due to traffic accidents in Russian cities, compared with many developed countries, are still extremely high. The methods of work carried out are: analysis of existing traffic problems in Volgograd, search for methods for solving these problems, practical implementation and evaluation of the quality of the measures carried out through modeling and evaluation of quality and road safety indicators. In Volgograd, over the past five years, only a decrease in the number of dead (from 90 to 58) has been achieved, the number of injured remains relatively stable from 1,200 to 1,600 people per year. In matters of road safety, given the statistics, you need to adopt more European experience than the US experience. The Vision Zero strategy is a benchmark in many countries around the world to improve road safety. A significant role in a large city, both for road safety and for capacity, is played by traffic lights. A methodology for improving the life quality of city residents and guests is proposed to improve mobility conditions by creating and developing places of attraction for residents, reducing the dominance of individual passenger transport, ensuring priority to public transport, developing a network of roads and managing traffic lights and road signs in real time.

This study investigates road signs put in place by Israel in Area C in the occupied West Bank. It discusses how language on road signs in the West Bank serve as tool that enforces Israeli dominance over the area and blurs the Palestinian existence. This dominance is reflected in the excessive placement of signs referring to Israeli settlements compared to signs referring to Palestinian communities, transliteration of Arabic names of sites into Hebrew, and deletion of Arabic, a language associated with the Palestinian identity, from road signs. In addition, this paper demonstrates how the exclusion of Arabic from road signs, especially brown signs referring to tourist attractions, serves as a barrier that aims at limiting Palestinians’ access to some parts of the West Bank and shows Israeli discriminatory practices against Palestinians. Moreover, this paper explains how warning signs at Israeli checkpoint construct the concept of the “Palestinian islands” by dividing the West Bank into disconnected enclaves surrounded by an ocean of Area C making the idea of establishing an independent Palestinian state seem impossible. Finally, this study records instances of Palestinians’ resistance to Israel through vandalism of signs that were placed by the Israeli authorities in Area C.