Pavement distress detection by stereo vision / Straßenzustandserkennung durch stereoskopische Bildverarbeitung

AbstractFor road maintenance up-to-date information about road conditions is important. Such information is currently expensive to obtain. Specially equipped measuring vehicles have to perform surface scans of the road, and it is unclear how to automatically Ąnd faulty sections in these scans. This research solves the problem by stereo vision with cameras mounted behind the windshield of a moving vehicle so that the system can easily be integrated into a large number of vehicles. The stereo images are processed into a depth map of the road surface. In a second step, color images from the cameras are combined with the depth map and are classified by a convolutional neural network. It is shown that the developed system is able to Ąnd defects that require knowledge about surface deformations. These defects could not have been found on monocular images. The images are taken at usual driving speed.

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The Road Maintenance Initiative

10.1596/0-8213-1859-4 ◽

1991 ◽

Cited By ~ 1

Author(s):

Steve Carapetis ◽

Hernan Levy ◽

Terje Wolden

Keyword(s):

Road Maintenance ◽

The Road

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Investigations of Road Wear Caused by Studded Tires

Tire Science and Technology ◽

10.2346/tire.14.420101 ◽

2014 ◽

Vol 42 (1) ◽

pp. 2-15

Author(s):

Johannes Gültlinger ◽

Frank Gauterin ◽

Christian Brandau ◽

Jan Schlittenhard ◽

Burkhard Wies

Keyword(s):

Traffic Safety ◽

Test Bench ◽

Operating Conditions ◽

Test Method ◽

Analytical Models ◽

The Road ◽

Driving Speed ◽

Major Factors ◽

Tire Friction ◽

Variable Operating Conditions

ABSTRACT The use of studded tires has been a subject of controversy from the time they came into market. While studded tires contribute to traffic safety under severe winter conditions by increasing tire friction on icy roads, they also cause damage to the road surface when running on bare roads. Consequently, one of the main challenges in studded tire development is to reduce road wear while still ensuring a good grip on ice. Therefore, a research project was initiated to gain understanding about the mechanisms and influencing parameters involved in road wear by studded tires. A test method using the institute's internal drum test bench was developed. Furthermore, mechanisms causing road wear by studded tires were derived from basic analytical models. These mechanisms were used to identify the main parameters influencing road wear by studded tires. Using experimental results obtained with the test method developed, the expected influences were verified. Vehicle driving speed and stud mass were found to be major factors influencing road wear. This can be explained by the stud impact as a dominant mechanism. By means of the test method presented, quantified and comparable data for road wear caused by studded tires under controllable conditions can be obtained. The mechanisms allow predicting the influence of tire construction and variable operating conditions on road wear.

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Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

Revista de Chimie ◽

10.37358/rc.20.6.8171 ◽

2020 ◽

Vol 71 (6) ◽

pp. 66-74

Author(s):

Younis M. Younis ◽

Salman H. Abbas ◽

Farqad T. Najim ◽

Firas Hashim Kamar ◽

Gheorghe Nechifor

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Diesel Fuel ◽

Back Propagation ◽

Second Step ◽

Heating Value ◽

Network Training ◽

Marquardt Algorithm ◽

Levenberg Marquardt ◽

Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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Porting A Convolutional Neural Network For Stereo Vision In Hardware

2020 25th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr48806.2021.9412633 ◽

2021 ◽

Author(s):

D. - Od. G. Sotiropoulos ◽

G. - P. K. Economou

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Stereo Vision

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Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images

Sensors ◽

10.3390/s21155137 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5137

Author(s):

Elham Eslami ◽

Hae-Bum Yun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Contextual Information ◽

Automated Classification ◽

Automated Recognition ◽

Pavement Distress ◽

Multi Scale ◽

Pavement Distresses ◽

Multi Class Classification ◽

Transportation Applications

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encoding contextual information through multi-scale input tiles and (ii) employing a mid-fusion approach with an attention module for heterogeneous image contexts from different input scales. A+MCNN is trained and tested with four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). A+MCNN is compared with four deep classifiers that are widely used in transportation applications and a generic CNN classifier (as the control model). The results show that A+MCNN consistently outperforms the baselines by 1∼26% on average in terms of the F-score. A comprehensive discussion is also presented regarding how these classifiers perform differently on different road objects, which has been rarely addressed in the existing literature.

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Estimation of Soil Surface Roughness Using Stereo Vision Approach

Sensors ◽

10.3390/s21134386 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4386

Author(s):

Afshin Azizi ◽

Yousef Abbaspour-Gilandeh ◽

Tarahom Mesri-Gundoshmian ◽

Aitazaz A. Farooque ◽

Hassan Afzaal

Keyword(s):

Surface Roughness ◽

Stereo Vision ◽

Agricultural Soils ◽

Depth Map ◽

Soil Surface ◽

Regression Equations ◽

Height Profile ◽

Peak Fitting ◽

Soil Surface Roughness ◽

Tillage Operations

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

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