FPGA-Based System for Road Signs Color Segmentation

2020 ◽  
pp. 62-68
Author(s):  
Hicham Hajoui Mouane ◽  
Rachid Allaoui ◽  
Aimad El Mourabit ◽  
Abdelhak Ezzine
Keyword(s):  
Color Segmentation ◽  
Road Signs

A New Novel Hybrid Dynamic Color Segmentation Model for Road Signs in Noisy Conditions

2021 ◽  
Vol 9 (3) ◽  
pp. 1-22
Author(s):  
Akram Abdel Qader
Keyword(s):  
Spatial Information ◽  
Histogram Equalization ◽  
Classification Systems ◽  
Color Segmentation ◽  
Safety Issues ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Noisy Conditions ◽  
Segmentation Task

Image segmentation is the most important process in road sign detection and classification systems. In road sign systems, the spatial information of road signs are very important for safety issues. Road sign segmentation is a complex segmentation task because of the different road sign colors and shapes that make it difficult to use specific threshold. Most road sign segmentation studies do good in ideal situations, but many problems need to be solved when the road signs are in poor lighting and noisy conditions. This paper proposes a hybrid dynamic threshold color segmentation technique for road sign images. In a pre-processing step, the authors use the histogram analysis, noise reduction with a Gaussian filter, adaptive histogram equalization, and conversion from RGB space to YCbCr or HSV color spaces. Next, a segmentation threshold is selected dynamically and used to segment the pre-processed image. The method was tested on outdoor images under noisy conditions and was able to accurately segment road signs with different colors (red, blue, and yellow) and shapes.

DETECTION OF TRAFFIC SIGNALS USING COLOR SEGMENTATION AND A RADIAL SYMMETRY DETECTOR

2021 ◽  
pp. 25-33
Author(s):  
Р.М. Шакирзянов
Keyword(s):  
Traffic Signals ◽  
Radial Symmetry ◽  
Unmanned Vehicles ◽  
Color Segmentation ◽  
Morphological Operations ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Traffic Lights ◽  
Emergency Situations ◽  
Road Signs

В настоящее время широкое распространение получают беспилотные системы управления различными транспортными средствами, в том числе автомобилями. Управление беспилотным автомобилем предполагает решение задач, связанных с распознаванием объектов дорожной обстановки: пешеходов, автомобилей, препятствий (в виде ям, кочек, столбов, деревьев, зданий и т.д.), дорожных знаков, разметки, светофоров. Предложен алгоритм решения задачи обнаружения и распознавания сигналов светофоров круглой формы. Для решения этой задачи задействованы: быстрое преобразование радиальной симметрии, цветовая сегментация, морфологические операции. Особенностью алгоритма является то, что области расположения световых сигналов предварительно определяются по цветовому признаку с последующим уточнением формы и положения объектов на изображении. На основе предложенного метода было разработано программное обеспечение для обнаружения сигналов светофоров на фотоснимках. Программное обеспечение было протестировано на общедоступной базе изображений, содержащей светофоры. Предлагаемый алгоритм показал работоспособность, он может быть расширен в части типов распознаваемых сигналов и применён в составе систем управления беспилотными транспортными средствами, а также в составе систем помощи водителю для решения задач по предупреждению опасных и аварийных ситуаций на транспорте Currently, unmanned systems for controlling various vehicles, including cars, are becoming widespread. Driving an unmanned vehicle involves solving problems related to the recognition of traffic objects: pedestrians, cars, obstacles (in the form of holes, bumps, poles, trees, buildings, etc.), road signs, markings, traffic lights. An algorithm for solving the problem of detecting and recognizing circular traffic signals is proposed. To solve this problem, the following are involved: rapid transformation of radial symmetry, color segmentation, morphological operations. A feature of the algorithm is that the areas of the location of the light signals are preliminarily determined by color, followed by the refinement of the shape and position of objects in the image. Based on the proposed method, software was developed for detecting traffic signals in photographs. The software was tested on a publicly available database of images containing traffic lights. The proposed algorithm has shown its efficiency, it can be expanded in terms of the types of signals recognized and used as part of control systems for unmanned vehicles, as well as part of driver assistance systems for solving problems to prevent dangerous and emergency situations

Comprehension and training of international road signs

2004 ◽  
Author(s):  
Shanna J. Ward ◽  
Michael S. Wogalter ◽  
Andrew W. Mercer
Keyword(s):  
Road Signs ◽  
And Training

Language, Politics and Society in the Middle East

2018 ◽  
Author(s):  
Abeer AlNajjar
Keyword(s):  
Middle East ◽  
Linguistic Landscape ◽  
Language Politics ◽  
Road Signs ◽  
Grassroots Initiatives ◽  
Language And Politics ◽  
Development Of Language ◽  
Core Questions ◽  
The Relationship ◽  
Shed Light

This book aims to shed light on core questions relating to language and society, language and conflict, and language and politics, in relation to a changing Middle East. While the book focuses on Arabic, it goes way beyond a purely linguistic analysis by bringing to the fore a set of pressing questions about the relationship between Arabic and society. For example, it touches on the development of language policy via an examination of administrative mandates (top-down) in contrast to grassroots initiatives (bottom-up); the deeper layers of the linguistic landscape that highlight the connection between politics, conflict, identity, road signs and street names; Arabic studies and Arabic identity and the myriad ways countries deal simultaneously with globalisation while also seeking to strengthen local and national identity, and more.

scholarly journals Deep Neural Networks for Road Sign Detection and Embedded Modeling Using Oblique Aerial Images

2021 ◽  
Vol 13 (5) ◽  
pp. 879
Author(s):  
Zhu Mao ◽  
Fan Zhang ◽  
Xianfeng Huang ◽  
Xiangyang Jia ◽  
Yiping Gong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Smart Cities ◽  
Geometric Constraints ◽  
Small Object ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Synthesis Strategy ◽  
Urban Models ◽  
Sign Detection

Oblique photogrammetry-based three-dimensional (3D) urban models are widely used for smart cities. In 3D urban models, road signs are small but provide valuable information for navigation. However, due to the problems of sliced shape features, blurred texture and high incline angles, road signs cannot be fully reconstructed in oblique photogrammetry, even with state-of-the-art algorithms. The poor reconstruction of road signs commonly leads to less informative guidance and unsatisfactory visual appearance. In this paper, we present a pipeline for embedding road sign models based on deep convolutional neural networks (CNNs). First, we present an end-to-end balanced-learning framework for small object detection that takes advantage of the region-based CNN and a data synthesis strategy. Second, under the geometric constraints placed by the bounding boxes, we use the scale-invariant feature transform (SIFT) to extract the corresponding points on the road signs. Third, we obtain the coarse location of a single road sign by triangulating the corresponding points and refine the location via outlier removal. Least-squares fitting is then applied to the refined point cloud to fit a plane for orientation prediction. Finally, we replace the road signs with computer-aided design models in the 3D urban scene with the predicted location and orientation. The experimental results show that the proposed method achieves a high mAP in road sign detection and produces visually plausible embedded results, which demonstrates its effectiveness for road sign modeling in oblique photogrammetry-based 3D scene reconstruction.

scholarly journals Design, manufacture and testing of a prototype obstacle-detection device at blind corners: an automated highway system technology

2020 ◽  
Author(s):  
Charles Atombo ◽  
Emmanuel Gbey ◽  
Apevienyeku Kwami Holali
Keyword(s):  
Traffic Accidents ◽  
Low Cost ◽  
Effective Area ◽  
Obstacle Detection ◽  
The Road ◽  
Vibration Sensors ◽  
Road Signs ◽  
Sensor Module ◽  
Vehicle Path ◽  
Automated Highway

Abstract Traffic accidents on highways are attributed mostly to the "invisibility" of oncoming traffic and road signs. "Speeding" also causes drivers to reduce the effective radius of the vehicle path in the curve, thus trespassing into the lane of the oncoming traffic. The main aim of this paper was to develop a multisensory obstacle-detection device that is affordable, easy to implement and easy to maintain to reduce the risk of road accidents at blind corners. An ultrasonic sensor module with a maximum measuring angle of 15° was used to ensure that a significant portion of the lane was detected at the blind corner. The sensor covered a minimum effective area of 0.5 m2 of the road for obstacle detection. Yellow light was employed to signify caution while negotiating the blind corner. Two photoresistors (PRs) were used as sensors because of the limited number of pins on the microcontroller (Arduino Uno). However, the device developed for this project achieved obstacle detection at blind corners at relatively low cost and can be accessed by all road users. In real-world applications, the use of piezoelectric accelerometers (vibration sensors) instead of PR sensors would be more desirable in order to detect not only cars but also two-wheelers.

scholarly journals Shining Light on Retroreflectivity: Perceived Brightness of Surfaces with Varying Reflective Properties

2020 ◽  
Vol 64 (1) ◽  
pp. 1815-1819
Author(s):  
Savana L. King ◽  
Ellen C. Szubski ◽  
Richard A. Tyrrell
Keyword(s):  
Entrance Angle ◽  
Road Signs ◽  
Road Users ◽  
Vulnerable Road Users

Typical road users appear not to understand retroreflectivity despite nightly exposure to retroreflective materials like road signs. A critical benefit of retroreflective materials is a robustness to changes in entrance angle, the angle at which light strikes the material. This study aims to measure observers’ perceived brightness judgments of surfaces representing three types of reflection (diffuse, specular, and retroreflective) when viewed under manipulations of entrance angle. Perceived brightness will be assessed before and during a demonstration including illumination from a source positioned near the observer’s eyes. Prior to the demonstration, observers are hypothesized to predict specular and retroreflective surfaces will have a consistent brightness despite changes in entrance angle. Seeing the retroreflectivity demo is expected to result in increased perceived brightness of only the retroreflective surfaces in the more extreme entrance angle conditions. Watching visual demonstrations of reflection, however, is expected to produce an enhanced appreciation that retroreflective (but not specular or diffuse) surfaces remain bright despite large changes in entrance angle. This evidence may eventually increase demand for retroreflective markings by vulnerable road users.

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