The Road Sign Problem Revisited

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.

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Road Sign System Design for Aging Population

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.869-870.247 ◽

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.

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

International Journal of Software Innovation ◽

10.4018/ijsi.2021070101 ◽

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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Determination of ASI and THIV parameters based on the results of experimental and numerical research in relation to EU standards

MATEC Web of Conferences ◽

10.1051/matecconf/202133801025 ◽

2021 ◽

Vol 338 ◽

pp. 01025

Author(s):

Michał Stopel

Keyword(s):

Experimental Tests ◽

Severity Index ◽

Point Of View ◽

The Finite Element Method ◽

Safety Level ◽

The Road ◽

Road Sign ◽

Numerical Research ◽

Python Programming

Determining the values of ASI (Acceleration Severity Index) and THIV (Theoretical Head Impact Velocity) parameters during tests allows you to assign an appropriate class for a given type of object to determine the safety level and to give the CE marking. The paper presents the methodology for determining these parameters based on the EN 1317-1 and EN 12767 standards. The paper also presents a tool created with the use of the Python programming language, which, based on the results of experimental tests or the results of numerical calculations, allows to determine the ASI and THIV values. The values of key parameters from the point of view of normative tests were calculated based on the results of experimental tests of the road sign supporting mast and numerical analysis carried out for the same case using the Finite Element Method and LS-Dyna software, following the EN 12767 standard.

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AUTOMATIC ROAD SIGN INVENTORY USING MOBILE MAPPING SYSTEMS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-717-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 717-723 ◽

Cited By ~ 4

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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