Road Sign System Design for Aging Population

2013 ◽  
Vol 869-870 ◽  
pp. 247-250
Author(s):  
Wen Li Lu ◽  
Ming Wei Liu
Keyword(s):  
Older People ◽  
System Design ◽  
Experimental Method ◽  
The Elderly ◽  
Efficient System ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Sign System ◽  
Board System

With the growth with the citys population of elderly people, the symptoms of aging are becoming more and more significant. Older people are faced with complex circumstances when they are outdoors, a correct and efficient system of road signs should help them reach their destinations safely. Therefore, a well designed system for the elderly is vital. The following research is concentrated on the design of the road sign system focusing upon the aspects of placement positions, height of the text and symbols, and the amount of information included on the sign. This will assist in the design of the most useful and efficient sign board system for the elderly. This will be determined through the experimental method.

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.

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.

scholarly journals AUTOMATIC ROAD SIGN INVENTORY USING MOBILE MAPPING SYSTEMS

2016 ◽  
Vol XLI-B3 ◽  
pp. 717-723 ◽  
Author(s):  
M. Soilán ◽  
B. Riveiro ◽  
J. Martínez-Sánchez ◽  
P. Arias
Keyword(s):  
Point Cloud ◽  
Road Network ◽  
Spatial Information ◽  
Maintenance Planning ◽  
Mobile Mapping ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Mobile Mapping Systems ◽  
Mapping Systems

The periodic inspection of certain infrastructure features plays a key role for road network safety and preservation, and for developing optimal maintenance planning that minimize the life-cycle cost of the inspected features. Mobile Mapping Systems (MMS) use laser scanner technology in order to collect dense and precise three-dimensional point clouds that gather both geometric and radiometric information of the road network. Furthermore, time-stamped RGB imagery that is synchronized with the MMS trajectory is also available. In this paper a methodology for the automatic detection and classification of road signs from point cloud and imagery data provided by a LYNX Mobile Mapper System is presented. First, road signs are detected in the point cloud. Subsequently, the inventory is enriched with geometrical and contextual data such as orientation or distance to the trajectory. Finally, semantic content is given to the detected road signs. As point cloud resolution is insufficient, RGB imagery is used projecting the 3D points in the corresponding images and analysing the RGB data within the bounding box defined by the projected points. The methodology was tested in urban and road environments in Spain, obtaining global recall results greater than 95%, and F-score greater than 90%. In this way, inventory data is obtained in a fast, reliable manner, and it can be applied to improve the maintenance planning of the road network, or to feed a Spatial Information System (SIS), thus, road sign information can be available to be used in a Smart City context.

scholarly journals Application on character recognition system on road sign for visually impaired: case study approach and future

2020 ◽  
Vol 10 (1) ◽  
pp. 778
Author(s):  
Jaejoon Kim
Keyword(s):  
Phase I ◽  
Visual Information ◽  
Character Recognition ◽  
Visually Impaired ◽  
Recognition System ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Text Information ◽  
On The Road

Many visually impaired people worldwide are unable to travel safely and autonomously because they are physically unable to perceive effective visual information during their daily lives. In this research, we study how to extract the character information of the road sign and transmit it to the visually impaired effectively, so they can understand easier. Experimental method is to apply the Maximally Stable External Region and Stroke Width Transform method in Phase I so that the visually impaired person can recognize the letters on the road signs. It is to convey text information to the disabled. The result of Phase I using samples of simple road signs was to extract the sign information after dividing the exact character area, but the accuracy was not good for the Hangul (Korean characters) information. The initial experimental results in the Phase II succeeded in transmitting the text information on Phase I to the visually impaired. In the future, it will be required to develop a wearable character recognition system that can be attached to the visually impaired. In order to perform this task, we need to develop and verify a miniaturized and wearable character recognition system. In this paper, we examined the method of recognizing road sign characters on the road and presented a possibility that may be applicable to our final development.

scholarly journals ROAD SAFETY AWARENESS AMONG UNIVERSITY STUDENTS: CASE STUDY AT MALAYSIA UNIVERSITI TUN HUSSEIN ONN MALAYSIA, JOHOR

2021 ◽  
Vol 12 (1) ◽  
pp. 56-65
Author(s):  
R. Abd Rahman ◽  
H. A. Mazle ◽  
W. M. Lim ◽  
M. I. Mohd Masirin ◽  
M. F. Hassan
Keyword(s):  
University Students ◽  
Road Safety ◽  
Speed Limit ◽  
The Road ◽  
Young Drivers ◽  
Road Signs ◽  
Road Sign ◽  
Safety Awareness ◽  
On The Road

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.

scholarly journals Detection and Validation of Tow-Away Road Sign Licenses through Deep Learning Methods

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4147 ◽  
Author(s):  
Fabrizio Balducci ◽  
Donato Impedovo ◽  
Giuseppe Pirlo
Keyword(s):  
Deep Learning ◽  
Industrial Applications ◽  
The Public ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Italian Law ◽  
Learning Techniques ◽  
Private Car ◽  
On The Road

This work presents the practical design of a system that faces the problem of identification and validation of private no-parking road signs. This issue is very important for the public city administrations since many people, after receiving a code that identifies the signal at the entrance of their private car garage as valid, forget to renew the code validity through the payment of a city tax, causing large money shortages to the public administration. The goal of the system is twice since, after recognition of the official road sign pattern, its validity must be controlled by extracting the code put in a specific sub-region inside it. Despite a lot of work on the road signs’ topic having been carried out, a complete benchmark dataset also considering the particular setting of the Italian law is today not available for comparison, thus the second goal of this work is to provide experimental results that exploit machine learning and deep learning techniques that can be satisfactorily used in industrial applications.

scholarly journals Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6777
Author(s):  
Jianwei Zhao ◽  
Jianhua Fang ◽  
Shouzhong Wang ◽  
Kun Wang ◽  
Chengxiang Liu ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Visual Information ◽  
Ultrasonic Sensor ◽  
Intelligent Robot ◽  
Distance Information ◽  
The Road ◽  
Additional Information ◽  
Road Signs ◽  
Road Sign ◽  
Control Command

The existing ultrasonic obstacle avoidance robot only uses an ultrasonic sensor in the process of obstacle avoidance, which can only be avoided according to the fixed obstacle avoidance route. Obstacle avoidance cannot follow additional information. At the same time, existing robots rarely involve the obstacle avoidance strategy of avoiding pits. In this study, on the basis of ultrasonic sensor obstacle avoidance, visual information is added so the robot in the process of obstacle avoidance can refer to the direction indicated by road signs to avoid obstacles, at the same time, the study added an infrared ranging sensor, so the robot can avoid potholes. Aiming at this situation, this paper proposes an intelligent obstacle avoidance design of an autonomous mobile robot based on a multi-sensor in a multi-obstruction environment. A CascadeClassifier is used to train positive and negative samples for road signs with similar color and shape. A multi-sensor information fusion is used for path planning and the obstacle avoidance logic of the intelligent robot is designed to realize autonomous obstacle avoidance. The infrared sensor is used to obtain the environmental information of the ground depression on the wheel path, the ultrasonic sensor is used to obtain the distance information of the surrounding obstacles and road signs, and the information of the road signs obtained by the camera is processed by the computer and transmitted to the main controller. The environment information obtained is processed by the microprocessor and the control command is output to the execution unit. The feasibility of the design is verified by analyzing the distance acquired by the ultrasonic sensor, infrared distance measuring sensors, and the model obtained by training the sample of the road sign, as well as by experiments in the complex environment constructed manually.

scholarly journals A Research Paper on Convolutional Neural Network (CNN) Theory based Automated Traffic and Road Sign Detection and Recognition System

2021 ◽  
Vol 9 (VII) ◽  
pp. 3775-3783
Author(s):  
Parkavi J.
Keyword(s):  
Road Safety ◽  
Road Network ◽  
Good Accuracy ◽  
Recognition System ◽  
Accuracy Rate ◽  
Traffic System ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Sign Detection

India is a country with a dense road network and has a complex system to maintain road safety. As we all know that we have a complex traffic system in which we have more than 100 types of traffic symbols in it. While driving, it is tough to take care of all the symbols placed at the road end. Sometimes the driver does not know what that symbol says. In this system sometimes the driver misses the road signs because the attention of the driver is overdriving the vehicle safe which leads to an accident or issuing Challan. Sometimes the traffic signs don't notice by the driver. So all the drivers or the vehicle need a system which is capable to read and recognize the traffic symbol placed at the road end and the system must be capable of giving simple instruction to the driver. So that system can automatically detect which type of symbol is this and can notify the driver. The system must have a good accuracy rate, as well as the system, must have a very good speed of working. This system can also be used in driverless cars to notify the system about the road signals and hence the system can tackle all the symbols carefully.

A Robust System for Road Sign Detection and Classification Using LeNet Architecture Based on Convolutional Neural Network

2020 ◽  
pp. 57-75
Author(s):  
Amal Bouti ◽  
Mohamed Adnane Mahraz ◽  
Jamal Riffi ◽  
Hamid Tairi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Accuracy Rate ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Sign Detection ◽  
Robust System

In this chapter, the authors report a system for detection and classification of road signs. This system consists of two parts. The first part detects the road signs in real time. The second part classifies the German traffic signs (GTSRB) dataset and makes the prediction using the road signs detected in the first part to test the effectiveness. The authors used HOG and SVM in the detection part to detect the road signs captured by the camera. Then they used a convolutional neural network based on the LeNet model in which some modifications were added in the classification part. The system obtains an accuracy rate of 96.85% in the detection part and 96.23% in the classification part.

scholarly journals AUTOMATIC ROAD SIGN INVENTORY USING MOBILE MAPPING SYSTEMS

2016 ◽  
Vol XLI-B3 ◽  
pp. 717-723 ◽  
Author(s):  
M. Soilán ◽  
B. Riveiro ◽  
J. Martínez-Sánchez ◽  
P. Arias
Keyword(s):  
Point Cloud ◽  
Road Network ◽  
Spatial Information ◽  
Maintenance Planning ◽  
Mobile Mapping ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Mobile Mapping Systems ◽  
Mapping Systems

The periodic inspection of certain infrastructure features plays a key role for road network safety and preservation, and for developing optimal maintenance planning that minimize the life-cycle cost of the inspected features. Mobile Mapping Systems (MMS) use laser scanner technology in order to collect dense and precise three-dimensional point clouds that gather both geometric and radiometric information of the road network. Furthermore, time-stamped RGB imagery that is synchronized with the MMS trajectory is also available. In this paper a methodology for the automatic detection and classification of road signs from point cloud and imagery data provided by a LYNX Mobile Mapper System is presented. First, road signs are detected in the point cloud. Subsequently, the inventory is enriched with geometrical and contextual data such as orientation or distance to the trajectory. Finally, semantic content is given to the detected road signs. As point cloud resolution is insufficient, RGB imagery is used projecting the 3D points in the corresponding images and analysing the RGB data within the bounding box defined by the projected points. The methodology was tested in urban and road environments in Spain, obtaining global recall results greater than 95%, and F-score greater than 90%. In this way, inventory data is obtained in a fast, reliable manner, and it can be applied to improve the maintenance planning of the road network, or to feed a Spatial Information System (SIS), thus, road sign information can be available to be used in a Smart City context.

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