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 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 Integration of Image-Based Fog Detection with Autonomous Decision System for Intelligent Road Sign

2021 ◽  
Vol 51 (3) ◽  
pp. 19-36
Author(s):  
Wojciech Chmiel ◽  
Jan Derkacz ◽  
Andrzej Dziech ◽  
Janusz Gozdecki ◽  
Stanisław Jędrusik ◽  
...  
Keyword(s):  
Road Traffic ◽  
Real Life ◽  
Decision System ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
On The Road ◽  
Analysis System ◽  
Rule System ◽  
Fog Detection

Abstract The paper presents the description of the decision system implemented for Intelligent Road Signs. It focuses on the implementation of the novel air transparency analysis system and its integration with the rule system and the speed control infrastructure. Moreover, there are presented issues of making decisions about the content displayed in the case of autonomous and cooperating signs. To reflect more closely on real-life situations, it is assumed that the content presented by the IRS changes dynamically, depending on the road traffic and weather parameters. The IRS system operation was presented using fog detection as an example.

scholarly journals USING DEEP LEARNING TO DIGITIZE ROAD ARROW MARKINGS FROM LIDAR POINT CLOUD DERIVED IMAGES

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 123-129
Author(s):  
M. L. R. Lagahit ◽  
Y. H. Tseng
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Semantic Information ◽  
High Definition ◽  
The Road ◽  
Learning Techniques ◽  
Local Coordinates ◽  
On The Road ◽  
Self Driving Cars

Abstract. The concept of Autonomous vehicles or self-driving cars has recently been gaining a lot of popularity. Because of this, a lot of research is being done to develop the technology. One of which is High Definition (HD) Maps, which are centimeter-level precision 3D maps that contain a lot of geometric and semantic information about the road which can assist the AV when driving. An important component of HD maps is the road markings which indicates a set of rules on how a vehicle should navigate itself on the road. For example, lane lines indicate which part of the road a vehicle can drive on in a certain direction. This research proposes a methodology that uses deep learning techniques to detect road arrows, road markings that show possible driving directions, on LIDAR derived images, and extract them as polyline vector shapefiles. The general workflow consists of (1) converting the LIDAR point cloud to images, (2) training and applying U-Net – a fully convolutional neural network, (3) creating masks from image segmentation results that have been transformed to fit the local coordinates, (4) extracting the polygons and polylines, and finally (5) exporting the vectors in shapefile format. The proposed methodology has shown promising results with object segmentation accuracies comparable with previous related works.

scholarly journals Green Deal as a Climate Changes Dam on the Road to Health

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Jevtic ◽  
C Bouland
Keyword(s):  
Public Health ◽  
Climate Change ◽  
Circular Economy ◽  
Clinical Medicine ◽  
Health Sector ◽  
The Public ◽  
The Road ◽  
National Public Health ◽  
On The Road ◽  
The Impact

Abstract Public health professionals (PHP) have a dual task in climate change. They should persuade their colleagues in clinical medicine of the importance of all the issues covered by the GD. The fact that the health sector contributes to the overall emissions of 4.4% speaks to the lack of awareness within the health sector itself. The issue of providing adequate infrastructure for the health sector is essential. Strengthening the opportunities and development of the circular economy within healthcare is more than just a current issue. The second task of PHP is targeting the broader population. The public health mission is being implemented, inter alia, through numerous activities related to environmental monitoring and assessment of the impact on health. GD should be a roadmap for priorities and actions in public health, bearing in mind: an ambitious goal of climate neutrality, an insistence on clean, affordable and safe energy, a strategy for a clean and circular economy. GD provides a framework for the development of sustainable and smart transport, the development of green agriculture and policies from field to table. It also insists on biodiversity conservation and protection actions. The pursuit of zero pollution and an environment free of toxic chemicals, as well as incorporating sustainability into all policies, is also an indispensable part of GD. GD represents a leadership step in the global framework towards a healthier future and comprises all the non-EU members as well. The public health sector should consider the GD as an argument for achieving goals at national levels, and align national public health policies with the goals of this document. There is a need for stronger advocacy of health and public-health interests along with incorporating sustainability into all policies. Achieving goals requires the education process for healthcare professionals covering all of topics of climate change, energy and air pollution to a much greater extent than before.

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.

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.

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.

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