scholarly journals Classification and Segmentation of Longitudinal Road Marking Using Convolutional Neural Networks for Dynamic Retroreflection Estimation

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5560
Author(s):  
Chanjun Chun ◽  
Taehee Lee ◽  
Sungil Kwon ◽  
Seung-Ki Ryu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Road Safety ◽  
Estimation Method ◽  
The Road ◽  
Classification And Regression ◽  
Road Marking ◽  
Reference Plate

Road markings constitute one of the most important elements of the road. Moreover, they are managed according to specific standards, including a criterion for a luminous contrast, which can be referred to as retroreflection. Retroreflection can be used to measure the reflection properties of road markings or other road facilities. It is essential to manage retroreflection in order to improve road safety and sustainability. In this study, we propose a dynamic retroreflection estimation method for longitudinal road markings, which employs a luminance camera and convolutional neural networks (CNNs). The images that were captured by a luminance camera were input into a classification and regression CNN model in order to determine whether the longitudinal road marking was accurately acquired. A segmentation model was also developed and implemented in order to accurately present the longitudinal road marking and reference plate if a longitudinal road marking was determined to exist in the captured image. The retroreflection was dynamically measured as a driver drove along an actual road; consequently, the effectiveness of the proposed method was demonstrated.

scholarly journals The Evaluation of Experimental Variables for Sustainable Virtual Road Safety Audits

2021 ◽  
Vol 13 (11) ◽  
pp. 5899
Author(s):  
Yeonsoo Jun ◽  
Juneyoung Park ◽  
Chunho Yeom
Keyword(s):  
Road Safety ◽  
Traffic Light ◽  
Scenario Development ◽  
Driving Simulators ◽  
Traffic Condition ◽  
The Road ◽  
Road Pavement ◽  
Net Promoter ◽  
Road Safety Audits ◽  
Road Marking

This paper evaluates experimental variables for virtual road safety audits (VRSAs) through practical experiments to promote sustainable road safety. VRSAs perform road safety audits using driving simulators (DSs), and all objects in the road environment cannot be experimental variables because of realistic constraints. Therefore, the study evaluates the likelihood of recommendation of VRSA experimental variables by comparing DSs experiments and field reviews to secure sustainable road safety conditions. The net promoter score results evaluated “Tunnel”, “Bridge”, “Underpass”, “Footbridge”, “Traffic island”, “Sign”, “Lane”, “Road marking”, “Traffic light”, “Median barrier”, “Road furniture”, and “Traffic condition” as recommended variables. On the contrary, the “Road pavement”, “Drainage”, “Lighting”, “Vehicle”, “Pedestrian”, “Bicycle”, “Accident”, and “Hazard event” variables were not recommended. The study can be used for decision making in VRSA scenario development as an initial effort to evaluate its experimental variables.

scholarly journals A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages

2020 ◽  
Vol 12 (5) ◽  
pp. 765 ◽  
Author(s):  
Calimanut-Ionut Cira ◽  
Ramon Alcarria ◽  
Miguel-Ángel Manso-Callejo ◽  
Francisco Serradilla
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Learning Strategies ◽  
Convolutional Neural Networks ◽  
Test Data ◽  
Road Network ◽  
Ideal Solution ◽  
The Road

Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. In addition, the road network plays an important part in transportation, and currently one of the main related challenges is detecting and monitoring the occurring changes in order to update the existent cartography. This task is challenging due to the nature of the object (continuous and often with no clearly defined borders) and the nature of remotely sensed images (noise, obstructions). In this paper, we propose a novel framework based on convolutional neural networks (CNNs) to classify secondary roads in high-resolution aerial orthoimages divided in tiles of 256 × 256 pixels. We will evaluate the framework’s performance on unseen test data and compare the results with those obtained by other popular CNNs trained from scratch.

QUALITY CONTROL OF HORIZONTAL ROAD MARKING APPLICATION DURING CERTIFICATION OF MARKING MACHINES

2019 ◽  
pp. 24-29
Author(s):  
Yuriy Hostev ◽  
Lev Rumiantsev ◽  
Tetyana Kostrulova
Keyword(s):  
Road Safety ◽  
High Performance ◽  
Paint Layer ◽  
Essential Factor ◽  
The Road ◽  
Machine Type ◽  
Road Signs ◽  
Liquid Paint ◽  
Road Marking

Horizontal road marking along with road signs is an essential factor that ensures road safety. The quality of road markings largely depends on the quality of the application. In today’s, for road marking are used road marking machines. They are complex high-performance automated units requiring highly professional operators. Stink to classify for the way of marking, design of the running gear, base machine, type of drive, productivity and the most important signs. The article describes the principles of the operation of marking machines and the main technical characteristics of machines from leading manufacturers: GRACO, SТiM, Hofmann. When applying road marking, special attention should be paid to the use of certified materials and certified equipment. The results of measuring the geometric and lighting indicators of horizontal road marking are presented. During certification, marking is applied with a marking machine. During the application, the thickness of the liquid paint layer is determined by a calibrated comb. To control the lighting technical characteristics of road marking – whiteness, retroreflectivity (Rl) and brightness (Qd) coefficients, a ZRM 1021 reflectometer and a ZRM 1013 retroreflectometer are used. Geometric indicators were measured with a tape measure. The table of certification results of the Line Lazer IV 3900 machine is shown. Similar indicators were obtained during certification of the machines of Hofmann, Combizet Gruen, RoadLazer, etc. Deviations from the road marking indicators declared by the manufacturer are not observed for 3 years. All marking performed by verified marking machines meets the requirements of current regulations. Keywords: certification of machines, durability of marking, retroreflectivity, accuracy of application, brightness.

scholarly journals Application of the LifeRoSE Method in Funcionality Evaluation of Road Safety Equipment

2020 ◽  
Vol 50 (3) ◽  
pp. 43-65
Author(s):  
Anna Gobis ◽  
Kazimierz Jamroz ◽  
Łukasz Jeliński
Keyword(s):  
Mathematical Model ◽  
Life Cycle ◽  
Road Safety ◽  
Estimation Method ◽  
Economic Effectiveness ◽  
Life Cycle Costs ◽  
Safety Equipment ◽  
The Impact ◽  
Road Marking

AbstractThe article presents a mathematical model of the life cycle estimation method of road safety equipment. Then the model was adjusted to estimate the life cycle costs of the chosen horizontal road marking. Using the LCC method, the functionality of the horizontal marking was evaluated in terms of efficiency, durability and economic effectiveness. The article also presents the impact of selected factors on the life cycle costs of the horizontal road marking.

scholarly journals Assessing Wear Out of Tyre using Opencv & Convolutional Neural Networks

2021 ◽  
Vol 2089 (1) ◽  
pp. 012001
Author(s):  
C Kishor Kumar Reddy ◽  
P R Anisha ◽  
R Madana Mohana
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Wear Testing ◽  
Timely Manner ◽  
Safe Driving ◽  
The Road ◽  
Tire Wear ◽  
Wear And Tear

Abstract This work proposes a process to detect the wear and tear of car tires. Tire is the only part of the road that does not interact with the road. The condition of the wheel should therefore be monitored in a timely manner for safe driving. Tired fatigue occurs due to limitations such as that the tread limit is less than 1.6 cm, the damage to the rubber, where there are pipes around 4 to 5, the affected tire. We look at some of the above limitations of tire wear testing using computer viewing techniques such as opencv and convolutional neural networks. Opencv and convolutional neural networks are widely used for object detection and image classification. We used these methods and obtained 90.90% accuracy, with which we can predict tire wear to avoid dangerous accidents..

scholarly journals System for monitoring road slippery based on CCTV cameras and convolutional neural networks

2020 ◽  
Vol 55 (3) ◽  
pp. 521-534
Author(s):  
Dariusz Grabowski ◽  
Andrzej Czyżewski
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Road Safety ◽  
Learning Method ◽  
Light Conditions ◽  
Video Cameras ◽  
Road Signs ◽  
Road Users ◽  
Network Topologies ◽  
Slippery Surface

Abstract The slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transfer-learning method employed to the processing of images acquired with video cameras. Based on photos taken in different light conditions by CCTV cameras located at the roadsides in Poland, four network topologies have been trained and tested: Resnet50 v2, Resnet152 v2, Vgg19, and Densenet201. The last-mentioned network has proved to give the best result with 98.34% accuracy of classification dry, wet, and snowy roads.

scholarly journals In-Situ Study of Road Marking Durability Using Glass Microbeads and Antiskid Aggregates as Drop-On Materials

2018 ◽  
Author(s):  
Andrés Coves-Campos ◽  
Luis Bañón ◽  
José Andrés Coves-García ◽  
Salvador Ivorra
Keyword(s):  
Road Safety ◽  
Significant Information ◽  
The Road ◽  
Road Users ◽  
Different Types ◽  
Main Factors ◽  
Rural Highway ◽  
Road Marking ◽  
Over Time

Road markings play an important role in road safety because they provide significant information to drivers about the road. For that reason, they must be replaced when they are not correctly perceived by road users. To analyse which are the main factors that affect road marking perception over time, a test section was designed in a two-lane rural highway, running actual traffic over 18 different types of markings fabricated with different combinations of drop-on materials. Chromatic coordinates, luminance and retroreflectivity of each sample were measured during 18 months in order to study their evolution over time. The results obtained show different behaviours depending on the aggregates and application method used. An increment of the durability has been observed with the use of different layers and mixtures of glass microbeads with different sizes.

scholarly journals Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5501 ◽  
Author(s):  
Chanjun Chun ◽  
Seung-Ki Ryu
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Supervised Learning ◽  
Convolutional Neural Networks ◽  
Surface Damage ◽  
Road Surface ◽  
Training Dataset ◽  
The Road ◽  
Fully Convolutional Neural Networks ◽  
On The Road

The various defects that occur on asphalt pavement are a direct cause car accidents, and countermeasures are required because they cause significantly dangerous situations. In this paper, we propose fully convolutional neural networks (CNN)-based road surface damage detection with semi-supervised learning. First, the training DB is collected through the camera installed in the vehicle while driving on the road. Moreover, the CNN model is trained in the form of a semantic segmentation using the deep convolutional autoencoder. Here, we augmented the training dataset depending on brightness, and finally generated a total of 40,536 training images. Furthermore, the CNN model is updated by using the pseudo-labeled images from the semi-supervised learning methods for improving the performance of road surface damage detection technique. To demonstrate the effectiveness of the proposed method, 450 evaluation datasets were created to verify the performance of the proposed road surface damage detection, and four experts evaluated each image. As a result, it is confirmed that the proposed method can properly segment the road surface damages.

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