Traffic Panels Research Articles

Arabic Text Detection on Traffic Panels in Natural Scenes

Identifying and acknowledging Traffic Panels (TP) and the text they display constitute significant use cases for Advanced Driver Assistance Systems (ADAS). In recent years, particularly in the context of the Arabic language, extracting textual information from TP and signs has emerged as a challenging problem in the field of computer vision. Furthermore, the significant rise in road traffic accidents within Arabic-speaking countries has resulted in substantial financial losses and loss of human lives. This is largely attributed to the limited number of diverse datasets for traffic signs and the absence of a reliable system for TP detection. Implementing warning and guidance systems for drivers on the road not only addresses this issue but also paves the way for the integration of intelligent components into future vehicles, offering decision support for transitioning to semi-automatic or fully automatic driving based on the driver’s health condition. These tasks present us with two main challenges. First, it involves developing a new Arabic dataset called the Syphax Traffic Panels dataset (STP) tailored to the diverse conditions of natural scenes gathered from “Sfax,” a city in Tunisia. This dataset aims to provide high-quality images of Arabic TP. Secondly, we suggest a deep learning method for detecting Arabic text on TP by evaluating the performance of the state-of-the-art algorithms in this context. In our study, we enhance the architecture of the most successful result achieved. The experiments conducted reveal promising results, affirming the significant contribution of our dataset to this research area, and even more encouraging results stemming from the enhancements made to the proposed method. The dataset we possess is accessible to the general public on IEEE DataPort https://dx.doi.org/10.21227/5zd9-pe55

HFENet: Hybrid Feature Enhancement Network for Detecting Texts in Scenes and Traffic Panels

HFENet: Hybrid Feature Enhancement Network for Detecting Texts in Scenes and Traffic Panels

Met-MLTS: Leveraging Smartphones for End-to-End Spotting of Multilingual Oriented Scene Texts and Traffic Signs in Adverse Meteorological Conditions

Intelligent systems, like driver assistance systems, remain within vehicles and help drivers by providing essential information about traffic, blockage of roads, and possible routes for safe driving. The objective of scene text spotting in a driver assistance system is to localize and recognize scene texts, signs of milestones, traffic panels, and road marks in natural scene images. However, text edges get fainted due to adverse weather conditions, like fog, rain, smog, or poor contrast. This makes the task of spotting more challenging. In this paper, we propose an end-to-end trainable deep neural network, known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Met-MLTS</i> , that can address the issue of spotting multi-oriented text instances in scene images captured in adverse meteorological conditions. It localizes words, predicts script class, and performs word spotting for every rotated bounding box. It is a fast multilingual scene text spotter that utilizes hierarchical spatial context, channel-wise inter-dependencies, and semantic edge supervision to localize and recognize words and predict script class in scene images using smartphones. We explore inter-class interference to reduce the misclassification problem. A light-weight recognition module for multilingual character segmentation, word-level recognition, and script identification is incorporated. We demonstrate the efficacy of our spotting network on resource-constraint devices.

ASAYAR: A Dataset for Arabic-Latin Scene Text Localization in Highway Traffic Panels

The extraction of text information from traffic panels is one of the challenging problems in computer vision. Although the past decade has seen a promising shift and important progress in object detection, few works and a limited number of datasets focus specifically on extracting text from traffic signs. To address the lack of data for text detection in traffic panels, especially those with Arabic scripts, this paper introduces a new multilingual and multipurpose dataset named ASAYAR. It consists of three sub-datasets: Arabic-Latin scene text localization, traffic sign detection, and directional symbol detection. The dataset contains 1763 images collected on different Moroccan highways, and annotated manually, using 16 object categories. The fully annotated ASAYAR images contains more than 20000 bounding box objects. The paper also investigates the usability of the dataset, by evaluating the performance of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">state-of-the-art</i> algorithms for object and text detection. Experimental results show good detection scores, demonstrating the potential contribution of ASAYAR in the development of methods for text extraction from traffic panels.

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm

Text-Based Traffic Panels Detection using the Tiny YOLOv3 Algorithm by Saba Kheirinejad 1,* , Noushin Riahi 2, Reza Azmi 2 1 Center for Ubiquitous Computing, University of Oulu, Oulu, Finland 2 Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran * Author to whom correspondence should be addressed. Journal of Engineering Research and

ATTICA: A Dataset for Arabic Text-Based Traffic Panels Detection

Detection and recognition of traffic panels and their textual information are important applications of advanced driving assistance systems (ADAS). They can significantly contribute in enhancing road safety by optimizing the driving experience. However, these tasks might face two major challenges. First, the lack of suitable and good quality datasets. Second, the in-feasibility of global standardization of traffic panels in terms of shape, color and language of the written text. Present research is intensively directed toward Latin text-based panels, while other scripts such as Arabic remain quiet undervalued. In this paper, we address this issue by introducing ATTICA, a new open-source multi-task dataset, consisting of two main sub-datasets: ATTICA_Sign for traffic signs/panels detection and ATTICA_Text for Arabic text extraction/recognition. Our dataset gathers 1215 images with 3173 traffic panel boxes, 870 traffic sign boxes and 7293 Arabic text boxes. In order to examine the utility and advantages of our dataset, we adopt state-of-the-art deep learning based approaches. The conducted experiments show promising results confirming the valuable addition of our dataset in this field of research.

Text Line Detection from Rectangle Traffic Panels of Natural Scene

Traffic sign detection and recognition is very important for Intelligent Transportation. Among traffic signs, traffic panel contains rich information. However, due to low resolution and blur in the rectangular traffic panel, it is difficult to extract the character and symbols. In this paper, we propose a coarse-to-fine method to detect the Chinese character on traffic panels from natural scenes. Given a traffic panel Color Quantization is applied to extract candidate regions of Chinese characters. Second, a multi-stage filter based on learning is applied to discard the non-character regions. Third, we aggregate the characters for text lines by Distance Metric Learning method. Experimental results on real traffic images from Baidu Street View demonstrate the effectiveness of the proposed method.

Robust detection and recognition of traffic signs on road panels

Traffic panels provide vital information on roads with the aid of iconic symbols and text strings. Recognising these signs accurately at the right time is crucial for car drivers to ensure safe journey. The automatic visual recognition and classification of the information contained in the panel could be very useful for driver assistance application. In this paper, a method is proposed to identify and to recognise the information contained in the traffic panels, as an application utilising colour space conversion for image acquisition then the colour decomposition and shape model generation using active appearance model (AAM). The traffic panel is detected using character descriptor-based adaptive fuzzy clustering (AFC). Multiframe detection strategy is a simple way of using precision and recall. Finally the prediction accuracy is computed by using massive training artificial neural network (MTANN). The efficiency of the system is evaluated with the help of the MATLAB-based experimental results which are compared with the bag of visual words for text geolocation and recognising symbols and texts (BTG & ST) method and shape and colour (SC)-based traffic sign detection methods in terms of the sensitivity, specificity and recognition rate.

Robust detection and recognition of traffic signs on road panels

Traffic panels provide vital information on roads with the aid of iconic symbols and text strings. Recognising these signs accurately at the right time is crucial for car drivers to ensure safe journey. The automatic visual recognition and classification of the information contained in the panel could be very useful for driver assistance application. In this paper, a method is proposed to identify and to recognise the information contained in the traffic panels, as an application utilising colour space conversion for image acquisition then the colour decomposition and shape model generation using active appearance model (AAM). The traffic panel is detected using character descriptor-based adaptive fuzzy clustering (AFC). Multiframe detection strategy is a simple way of using precision and recall. Finally the prediction accuracy is computed by using massive training artificial neural network (MTANN). The efficiency of the system is evaluated with the help of the MATLAB-based experimental results which are compared with the bag of visual words for text geolocation and recognising symbols and texts (BTG & ST) method and shape and colour (SC)-based traffic sign detection methods in terms of the sensitivity, specificity and recognition rate.

A REVIEW ON TEXT DETECTION TECHNIQUES

Text detection in image is an important field. Reading text in image is challenging because of the variations in images. Text detection in images is useful for many navigational purposes e.g. text on google API’s and traffic panels etc. This paper analyzes the work done on text detection by many researchers and critically evaluates the techniques designed for text detection and states the limitation of each approach. We have integrated the work of many researchers for getting a brief over view of multiple available techniques and their strengths and limitations are also discussed to give readers a clear picture. The major dataset discussed in all these papers are ICDAR 2003, 2005, 2011, 2013 and SVT(street view text).

Real Time Traffic Signboard Detection and Recognition from Street Level Imagery for Smart Vehicle

The text or symbol detection and recognition from traffic panels is a challenging problem. Number of important application areas is dependent on text detection and recognition, including advanced driver assistance systems, road surveying, and autonomous vehicles. In this research project a novel system for the automatic detection and recognition of text and symbol in traffic signs is proposed. Search regions with in the image must be defined. In this particular region locate a large number of candidates, which are then reduced by applying constraints based on temporal and structural information. This problem can be divided in two stages; First stage will be detection of region and second will be character recognition. The detection stage exploits knowledge of the structure of the scene, the size and location of the road in the frame. Once a potential traffic panels has been located, the next stage attempts to recognize text and symbols within the region. For the purpose of text detection MSER is used and for recognition purpose optical Character Recognition method is used. Automatic testing using XML files provide better accuracy.

Chinese Traffic Panels Detection and Recognition From Street-Level Images

Traffic sign detection and recognition has been the active research topic due to its potential applications in intelligent transportation. However, detection and recognition of traffic panels containing much information, still remains to be a challenging problem. This paper proposes a method to detect and recognize traffic panels from street-level images in the urban scenes and to analyze the information on them. The traffic panels are detected based on histogram of oriented gradient and linear support vector machines. The text strings and symbols on traffic panels are segmented using connected component analysis method. Finally, the symbols on traffic panels are recognized by means of a model named bag of spatial visual words. Experimental results on images from Baidu Panorama Map prove the effectiveness of the proposed method.

Histograms of Stroke Widths for Multi-script Text Detection and Verification in Road Scenes

Robust text detection and recognition in arbitrarily distributed, unrestricted images is a difficult problem, e.g. when interpreting traffic panels outdoors during autonomous driving. Most previous work in text detection considers only a single script, usually Latin, and it is not able to detect text with multiple scripts. Our contribution combines an established technique -Maximum Stable Extremal Regions-with a histogram of stroke width (HSW) feature and a Support Vector Machine classifier. We combined characters into groups by raycasting and merged aligned groups into lines of text that can also be verified by using the HSW. We evaluated our detection pipeline on our own dataset of road scenes from Autobahn (German Highways), and show how the character classifier stage can be trained with one script and be successfully tested on a different one. While precision and recall match to state of the art solution. A unique characteristic of the HSW feature is that it can learn and detect multiple scripts, which we believe can yield script independence.

A Review on Text Detection Techniques

Text detection in image is an important field. Reading text is challenging because of the variations in images. Text detection is useful for many navigational purposes e.g. text on google API’s and traffic panels etc. This paper analyzes the work done on text detection by many researchers and critically evaluates the techniques designed for text detection and states the limitation of each approach. We have integrated the work of many researchers for getting a brief over view of multiple available techniques and their strengths and limitations are also discussed to give readers a clear picture. The major dataset discussed in all these papers are ICDAR 2003, 2005, 2011, 2013 and SVT(street view text).

A CONTENT DISCLOSURE AND TEXT IDENTIFICATION BASED ON ROAD-LEVEL SYMBOLISM USING TCH ALGORITHM

Image enhancement is a process of improving the quality of image by improving its feature. Image contrast enrichment techniques have been largely studied in the past decades. Traffic sign disclosure and realization has been fully prepared for a great past. Traffic board detection and text recognition still leaving a protest in computer view due to its various group and the vast changeability of the data illustrated in them. The essential function can be to make an automated index of the traffic panels placed in a road to holding its care and to aid drivers. Then the figure are defined as a “bag of visual words” and restricted to applying support vector machines. Completely our own text detection and recognition method is tested on those images where a traffic board has been disclosure in system to naturally read and save the data details in the panels. Leading driver aid scheme could also use from text recognition for automated traffic signs and panels description. This visual appearance categorization method is a new approach for traffic panel detection in the state of the art. We propose a language model partly based on a dynamic dictionary for a limited geographical area using a reverse geocoding service. Experimental results on real images from Google Street View prove the efficiency of the proposed method and give way to using street-level images for different applications on ITS.

A CONTENT DISCLOSURE AND TEXT IDENTIFICATION BASED ON ROAD-LEVEL SYMBOLISM USING TCH ALGORITHM

Image enhancement is a process of improving the quality of image by improving its feature. Image contrast enrichment techniques have been largely studied in the past decades. Traffic sign disclosure and realization has been fully prepared for a great past. Traffic board detection and text recognition still leaving a protest in computer view due to its various group and the vast changeability of the data illustrated in them. The essential function can be to make an automated index of the traffic panels placed in a road to holding its care and to aid drivers. Then the figure are defined as a “bag of visual words” and restricted to applying support vector machines. Completely our own text detection and recognition method is tested on those images where a traffic board has been disclosure in system to naturally read and save the data details in the panels. Leading driver aid scheme could also use from text recognition for automated traffic signs and panels description. This visual appearance categorization method is a new approach for traffic panel detection in the state of the art. We propose a language model partly based on a dynamic dictionary for a limited geographical area using a reverse geocoding service. Experimental results on real images from Google Street View prove the efficiency of the proposed method and give way to using street-level images for different applications on ITS.

Text Detection and Recognition on Traffic Panels From Street-Level Imagery Using Visual Appearance

Traffic sign detection and recognition has been thoroughly studied for a long time. However, traffic panel detection and recognition still remains a challenge in computer vision due to its different types and the huge variability of the information depicted in them. This paper presents a method to detect traffic panels in street-level images and to recognize the information contained on them, as an application to intelligent transportation systems (ITS). The main purpose can be to make an automatic inventory of the traffic panels located in a road to support road maintenance and to assist drivers. Our proposal extracts local descriptors at some interest keypoints after applying blue and white color segmentation. Then, images are represented as a “bag of visual words” and classified using Naïve Bayes or support vector machines. This visual appearance categorization method is a new approach for traffic panel detection in the state of the art. Finally, our own text detection and recognition method is applied on those images where a traffic panel has been detected, in order to automatically read and save the information depicted in the panels. We propose a language model partly based on a dynamic dictionary for a limited geographical area using a reverse geocoding service. Experimental results on real images from Google Street View prove the efficiency of the proposed method and give way to using street-level images for different applications on ITS.

An auxiliary V2I network for road transport and dynamic environments

Since vehicle-to-infrastructure (V2I) communications require a major initial outlay and continuous maintenance, they have been less frequently implemented than vehicle-to-vehicle (V2V) communications in applications in the field of Intelligent Transportation Systems (ITS). Nevertheless, making use of the information provided by the infrastructure – traffic signals, traffic panels, and so on – can help improve the performance of various ITS applications. The present work describes the design and implementation of a low-cost infrastructure network based on ZigBee technology to alert drivers (or even override them by using active safety systems) in the case of some unexpected circumstance on the road so that they can prepare for the appropriate handling of their vehicles. Its implementation as an auxiliary network within a larger communications scheme allows the network’s load to be reduced and its performance to be improved. The proposed architecture was tested on a real car in a real scenario with quantified results for different applications.

Fatigue Testing of Traffic Tunnel Panels

An original method is presented for fatigue testing of tunnel panels that are dynamically loaded by pressure waves caused by car and truck traffic. The dynamic pressure load on the tunnel walls is measured at the site and simulated in a real-size laboratory testing procedure, in which pressure and suction loads are generated by a bellow system. The bellow system simulates the maximum loads caused by vans and small trucks. Additionally, a high frequency load is applied by means of a vibrator placed in the antinode of the most important mode shape of the panel system. The loading frequency is imposed close to the corresponding natural frequency of the panel and fixation system. In this vibration loading, the behavior of the panel system during the real life span is controlled. The testing system allows for optimization of both the panel composition and the attachment system.

New investigations concerning the relationships between congenital colour vision defects and road traffic security

New extentive experiments demonstrated that: (a) protan observers are more deficient than deutan ones with regard to perception distances of some traffic panels, of vehicle red stop lights, of vehicle red rear-position lights and of white, yellow and red reflectors. Contrarily, deutan observers are more deficient than protan ones for the distinction of differently coloured traffic lights and vehicle rear lights; (b) protan and deutan drivers are nevertheless not responsible for more traffic accidents than drivers with normal colour vision; (c) this apparent contradiction is due to psychological compensation mechanisms. The practical conclusions are: (a) that persons with defective colour vision need not to be excluded from non professional road traffic; (b) that it is nevertheless useful that they should be aware of their handicap; (c) that the red traffic signal has to be larger than the other ones; and (d) that the stop and red position lights of vehicles must be sufficiently intense and that the filters transmitting only pure red should be avoided in them.