Road Traffic Sign Recognition Research Articles

A Systematic Study Traffic Sign Detection System, Different Types of Road Traffic Sign and Various Techniques of Road Traffic Sign Recognition

A Systematic Study Traffic Sign Detection System, Different Types of Road Traffic Sign and Various Techniques of Road Traffic Sign Recognition

A Road Traffic Sign Recognition Method Based on Improved YOLOv5

A Road Traffic Sign Recognition Method Based on Improved YOLOv5

Road traffic sign recognition algorithm based on improved YOLOv4

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How Can Sustainable Public Transport Be Improved? A Traffic Sign Recognition Approach Using Convolutional Neural Network

Sustainable public transport is an important factor to boost urban economic development, and it is also an important part of building a low-carbon environmental society. The application of driverless technology in public transport injects new impetus into its sustainable development. Road traffic sign recognition is the key technology of driverless public transport. It is particularly important to adopt innovative algorithms to optimize the accuracy of traffic sign recognition and build sustainable public transport. Therefore, this paper proposes a convolutional neural network (CNN) based on k-means to optimize the accuracy of traffic sign recognition, and it proposes a sparse maximum CNN to identify difficult traffic signs through hierarchical classification. In the rough classification stage, k-means CNN is used to extract features, and improved support vector machine (SVM) is used for classification. Then, in the fine classification stage, sparse maximum CNN is used for classification. The research results show that the algorithm improves the accuracy of traffic sign recognition more comprehensively and effectively, and it can be effectively applied in unmanned driving technology, which will also bring new breakthroughs for the sustainable development of public transport.

Urban Road Traffic Sign Detection & Recognition with Time Space Relationship Model

Detection and recognition of urban road traffic signs is an important part of the Modern Intelligent Transportation System (ITS). It is a driver support function which can be used to notify and warn the driver for any possible incidence on the current stretch of road. This paper presents a robust and novel Time Space Relationship Model for high positive urban road traffic sign detection and recognition for a running vehicle. There are three main contributions of the proposed framework. Firstly, it applies fast color-segment algorithm based on color information to extract candidate areas of traffic signs and reduce the computation load. Secondly, it verifies the traffic sign candidate areas to decrease false positives and raise the accuracy by analysing the variation in preceding video-images sequence while implementing the proposed Time Space Relationship Model. Lastly, the classification is done with Support Vector Machine with dataset from real-time detection of TSRM. Experimental results indicate that the accuracy, efficiency, and the robustness of the framework are satisfied on urban road and detect road traffic sign in real time.

Road Traffic Sign Recognition and Vehicle Accident Avoidance System

Traffic sign recognition and vehicle accident avoidance system gets a of interest late by huge scale organizations, e.g., Apple, Google and Volkswagen and so on driven by the market requirements for smart applications, e.g. Automatic Driving and Driver Assistance Systems , Mobile Eye, Mobile Mapping and many more.In this paper, traffic sign recognition and vehicle accident avoidance system is utilized to keep up traffic and maintain a strategic distance from vehicle, caution the occupied drivers, and avoid activities that can lead a vehicle. An on-going programmed sign recognition and detection can support the driver with safety. System propose automated real time system which will capture the traffic sign and show it at driver dashboard with front obstacle exact distance on screen. The PiCam is associated with Raspberry Pi and it is utilized to capture pictures .Screen is utilized to show the system output e.g. appearing of traffic sign and separation of vehicle. This framework is configuration to maintain a strategic distance from vehicle happening on street.

Multi-Feature Fusion and Enhancement Single Shot Detector for Traffic Sign Recognition

Road traffic sign detection and recognition play an important role in advanced driver assistance systems (ADAS) by providing real-time road sign perception information. In this paper, we propose an improved (Single Shot Detector) SSD algorithm via multi-feature fusion and enhancement, named MF-SSD, for traffic sign recognition. First, low-level features are fused into high-level features to improve the detection performance of small targets in the SSD. We then enhance the features in different channels to detect the target by enhancing effective channel features and suppressing invalid channel features. Our algorithm gets good results in domestic real-time traffic signs. The proposed MF-SSD algorithm is evaluated with the German Traffic Sign Recognition Benchmark (GTSRB) dataset. The experimental results show that the MF-SSD algorithm has advantages in detecting small traffic signs. Compared with existing methods, it achieves higher detection accuracy, better efficiency, and better robustness in complex traffic environment.

Road traffic sign recognition algorithm based on computer vision

As road traffic sign recognition is a crucial component for automatic driver assistance systems, it is a key problem in computer vision as well. Therefore, in this paper, we study on the problem of road traffic sign recognition utilising the computer vision technology. The main innovation of this paper is to propose an improved convolutional neural network, and then use it to tackle the road traffic sign recognition problem. Convolutional neural network can learn features from training data set, and a convolutional network contains alternating layers of convolution and pooling. Particularly, RGB traffic images are transformed to grey scale images, and then grey scale images are input to the improved convolutional neural network. Furthermore, the fixed layers are utilised to discover region of interests, and the learnable layers are used to extract features. In general, output information of the proposed two learnable layers are input to the classifier separately, and parameters of learnable layers and the classifier are trained at the same time. Finally, GTSDB data set is chosen to make performance evaluation, among which 600 images and 300 images are regarded as training and testing data set respectively. Experimental results demonstrate that the improved CNN-based traffic sign recognition performs better than the traditional CNN.

An Approach towards Service System Building for Road Traffic Signs Detection and Recognition

The paper describes an approach towards road sign detection and recognition system. The model consists of two parts: localization and recognition. First one is responsible for the identification of region of interest by creating rectangular boxes marking off areas where signs are located. Recognition part takes these rectangular areas and uses data from those regions to tell which road traffic sign is located there. Correct classification is dependent on both parts working and communicating quickly, accurately and efficiently. It also shows the techniques used for the recognition and classification of the road signs in scientific environments and it describes the technology and the database of images that are widely used.

Improved VGG Model for Road Traffic Sign Recognition

Improved VGG Model for Road Traffic Sign Recognition

Road traffic sign recognition algorithm based on computer vision

Road traffic sign recognition algorithm based on computer vision

Advanced driver assistance system: Road sign identification using VIAPIX system and a correlation technique

We present a three-step approach based on the commercial VIAPIX® module for road traffic sign recognition and identification. Firstly, detection in a scene of all objects having characteristics of traffic signs is performed. This is followed by a first-level recognition based on correlation which consists in making a comparison between each detected object with a set of reference images of a database. Finally, a second level of identification allows us to confirm or correct the previous identification. In this study, we perform a correlation-based analysis by combining and adapting the Vander Lugt correlator with the nonlinear joint transformation correlator (JTC). Of particular significance, this approach permits to make a reliable decision on road traffic sign identification. We further discuss a robust scheme allowing us to track a detected road traffic sign in a video sequence for the purpose of increasing the decision performance of our system. This approach can have broad practical applications in the maintenance and rehabilitation of transportation infrastructure, or for drive assistance.

Robust road lanes and traffic signs recognition for driver assistance system

Increasing safety and reducing road accidents, thereby saving lives, are one of the great interests in the context of advanced driver assistance systems. Apparently, among the complex and challenging tasks of future intelligent vehicles is road lanes detection and road signs recognition. In this paper, a multitask driver assistance system has been proposed. First, the system provides the driver with real-time information from lanes markers and road signs, which consist of the most important and challenging tasks. Secondly, it generates an acoustic warning to the driver in advance of any danger. This warning then allows the driver to take appropriate corrective actions in order to mitigate or completely avoid the event. The proposed system was tested on real road scene captured from moving vehicle. From the experimental results, the system has demonstrated a robust performance for detecting the road lanes and signs under different conditions.

Hierarchical clustering of EMD based interest points for road sign detection

Abstract This paper presents an automatic road traffic signs detection and recognition system based on hierarchical clustering of interest points and joint transform correlation. The proposed algorithm consists of 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). We propose a two-step unsupervised clustering technique, which is adaptive and based on two criterion. In this context, the detected points are initially clustered based on the stable local features related to the brightness and color, which are extracted using Gabor filter. Then points belonging to each partition are reclustered depending on the dispersion of the points in the initial cluster using position feature. This two-step hierarchical clustering yields the possible candidate road signs or the region of interests (ROIs). Finally, a fringe-adjusted joint transform correlation (JTC) technique is used for matching the unknown signs with the existing known reference road signs stored in the database. The presented framework provides a novel way to detect a road sign from the natural scenes and the results demonstrate the efficacy of the proposed technique, which yields a very low false hit rate.

도로시설물 관리를 위한 교통안전표지 인식 및 자동위치 취득 방법 연구

PURPOSES: This study is to develop a road traffic sign recognition and automatic positioning for road facility management. METHODS: In this study, we installed the GPS, IMU, DMI, camera, laser sensor on the van and surveyed the car position, fore-sight image, point cloud of traffic signs. To insert automatic position of traffic sign, the automatic traffic sign recognition S/W developed and it can log the traffic sign type and approximate position, this study suggests a methodology to transform the laser point-cloud to the map coordinate system with the 3D axis rotation algorithm. RESULTS: Result show that on a clear day, traffic sign recognition ratio is 92.98%, and on cloudy day recognition ratio is 80.58%. To insert exact traffic sign position. This study examined the point difference with the road surveying results. The result RMSE is 0.227m and average is 1.51m which is the GPS positioning error. Including these error we can insert the traffic sign position within 1.51m CONCLUSIONS: As a result of this study, we can automatically survey the traffic sign type, position data of the traffic sign position error and analysis the road safety, speed limit consistency, which can be used in traffic sign DB.

Road Traffic Signs Recognition Using Genetic Algorithms and Neural Networks

Road Traffic Signs Recognition Using Genetic Algorithms and Neural Networks

Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle

This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.

Urban Road Traffic Sign Recognition Algorithm Based on Monocular Vision

Intelligent Transportation System (ITS) is a new industry and development greatly in recent years. The advanced electro-communication and computer technology combines detection, control, active warning and other information means. It provides a great guarantee for improving traffic safety and high efficiency. This paper puts forward the traffic rate-limiting sign and Lane Mark recognition method. In this study, four steps are applied to recognize a traffic and Lane Mark sign in an image. The first step, we transform the color space of image from RGB into HIS. Then the image blended HIS and RGB information is segmented. The third step, the text restrain and eliminate the isolated noise point based on eight-neighborhood. Finally, the traffic speed-limit sign and Lane Mark is recognized with template match. Our experiment results, in the out door, show that the method mentioned in the paper is useful and effective.

Research on Road Signs Recognition Based on Machine Vision

The road traffic signs recognition in open environment is an important research in the field of safe auxiliary driving, vehicle autonomous navigation and so on. Based on the characteristic of road signs, one-dimensional entropy division was carried on to segment the AOI image obtained by lane detection, and then the road signs were recognized by SVM with improved Hu moment features. Experiments show that the proposed method for road traffic signs recognition in open environment has good reliability and robustness