Review of remote sensing methodologies for pavement management and assessment

Schnebele, E.; Tanyu, Burak F.; Cervone, Guido; Waters, Nigel

doi:10.1007/s12544-015-0156-6

Cited by 165 publications

(100 citation statements)

References 83 publications

(69 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, such solutions are is simply not affordable by many countries and even less so for individual municipalities. Thus, researches have sought other methods to reduce the complexity and costs associated with surveying and inspecting road infrastructures, well documented in the literature [7,8]. As we will see in the next section, such efforts have mainly revolved around image processing and computer vision techniques, as the main information used by inspectors can be obtained from conventional imaging when dealing only with classification problems (and not properly surveying), which have made major strides in this domain due to the combination of efficient and lightweight deep learning-based generic object detectors and mobile computing technologies for inference purposes.…”

Section: Motivationmentioning

confidence: 99%

Road Damage Detection Acquisition System Based on Deep Neural Networks for Physical Asset Management

Angulo¹,

Vega-Fernández²,

Aguilar-Lobo³

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. So far, most studies focused only on the detection of the presence or absence of damages; however, in real-world scenarios, road managers from need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets available, leading to a lack of a common benchmark for road damage detection. Such dataset could be used for a great variety of applications; herein, it is intended to serve as the acquisition component of a physical asset management tool which can aid governments agencies for planning purposes, or by infrastructure maintenance companies, so they can implement predictive maintenance procedures.In this paper, we make two contributions to address these issues. First, we present a large-scale road damage dataset, which includes a more balanced and representative set of damages not present in previous studies. This dataset is composed of 18,0345 road damage images captured with a smartphone installed on a car, with 45,435 instances road surface damages (linear, lateral and alligator cracks, potholes, and various types of painting blurs). In order to generate this dataset, we obtained images from several public datasets and augmented it with crowdsourced images, which where manually annotated for further processing. The images were captured under a variety of weather and illumination conditions. In each image, we annotated the bounding box representing the location and type of damage and its extent. Second, we trained different types generic object detection methods, both traditional (an LBP-cascaded classifier) and deep learning-based, specifically, MobileNet and RetinaNet, which are amenable for embedded and mobile and implementations with an acceptable performance for many applications. We compared the accuracy and inference time of all these models with others in the state of the art, achieving higher accuracies in all the eight classes present in the dataset introduced by researchers at the University of Tokyo, and in other related works, with a lower inference time.

show abstract

Section: Motivationmentioning

confidence: 99%

Road Damage Detection Acquisition System Based on Deep Neural Networks for Physical Asset Management

Angulo¹,

Vega-Fernández²,

Aguilar-Lobo³

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…In transportation engineering, despite considerable information can be derived from new technologies and can be incorporated into the traditional performance measurements (see e.g. [2][3][4][5][6][7][8]), the effect of variability and uncertainty in input parameters on outputs is not often taken into account in the capacity analysis of roads and intersections. The assessment of the effects of a design choice on one or more parameters that are used when an operational analysis is being carried out, requests information on the sources of uncertainty that have affected them and the relation among them [9].…”

Section: The Backgroundmentioning

confidence: 99%

Exploring the uncertainty in capacity estimation at roundabouts

Giuffrè

Granà

Tumminello

2017

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

Purpose In gap-acceptance theory the critical and the followup headways have a significant role in determining roundabout entry capacities which in turn depend on circulating flow rates under a specified arrival headway distribution. Calculation considers single mean values of the gap-acceptance parameters, neglecting the inherent variations in these random variables and providing a single value of entry capacity. The purpose of this paper is to derive the entry capacity distribution which accounts for the variations of the contributing (random) variables and suggest how to consider this issue in the operational analysis of the roundabouts. Methods We performed a Monte Carlo simulation to get the distribution of entry capacity and found Crystal Ball software effective for performing the random sampling from the probability density functions of each contributing parameter. A steady-state model of capacity was used for performing many runs; in each run, the values of each contributing parameter were randomly drawn from the corresponding distributions. Results The paper presents the first simulations and the entry capacity distributions at roundabouts, once the probability distributions of the headways were assumed. The results of the analysis were expressed probabilistically, meaning that the probability distributions of capacity rather than the simple point estimates were obtained. Conclusions Comparing the capacity values based on a metaanalytic estimation of critical and follow-up headways and the capacity functions based on the probability distributions of the model parameters, more insights in developing an appropriate approach to capacity estimation at roundabouts can be gained.

show abstract

“…Previously, time-consuming field investigations and manual measurements were the traditional methods to detect and evaluate the pavement distresses, many of which were destructive to the road surface meanwhile (Eriksson et al, 2008). Currently, with the support of computer and remote sensing technologies, many forms of remote sensing data without destructive effect on pavement and some advanced pattern recognition algorithms are introduced into the detection of pavement damages, such as digital images, LiDAR and Radar (Mettas et al, 2015;Schnebele et al, 2015;Zhang & Bogus, 2014). Pavement Management System is one highly integrated system with some types of sophisticated remote sensing sensors, which is commonly mounted on a mobile vehicle to collect the remote sensing data for pavement monitoring by majority of road departments (Schnebele et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, with the support of computer and remote sensing technologies, many forms of remote sensing data without destructive effect on pavement and some advanced pattern recognition algorithms are introduced into the detection of pavement damages, such as digital images, LiDAR and Radar (Mettas et al, 2015;Schnebele et al, 2015;Zhang & Bogus, 2014). Pavement Management System is one highly integrated system with some types of sophisticated remote sensing sensors, which is commonly mounted on a mobile vehicle to collect the remote sensing data for pavement monitoring by majority of road departments (Schnebele et al, 2015). Digital pavement images are the most commonly used data type that can be used to extract the features of pavement distresses, such as spectral features, geometry features and texture features (Koch, et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Object-Based and Supervised Detection of Potholes and Cracks From the Pavement Images Acquired by Uav

Pan

Zhang

Sun

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Roads are the basic element of land transportation system. After construction, the quality of road will decrease because of the aging and deterioration of the road surface. In the end, some distresses will appear on the pavement, such as the most common potholes and cracks. In order to improve the efficiency of pavement inspection, nowadays some new forms of remote sensing data without destructive effect on the pavement are widely used to detect the pavement distresses, such as digital images, LiDAR and Radar. In our study, the digital pavement images acquired by Unmanned Aerial Vehicle (UAV) and four popular supervised learning algorithms (KNN, SVM, ANN, RF) were used to distinguish between the normal pavement and pavement damages (i.e. cracks and potholes). Each of learning algorithms was given a series of different parameters, and the classification accuracy and computational time as two assessment criteria of the algorithm performance were calculated. Finally, four best models for each kind of learning algorithms were selected based on the standard of highest accuracy and minimum running time.

show abstract

Review of remote sensing methodologies for pavement management and assessment

Cited by 165 publications

References 83 publications

Road Damage Detection Acquisition System Based on Deep Neural Networks for Physical Asset Management

Road Damage Detection Acquisition System Based on Deep Neural Networks for Physical Asset Management

Exploring the uncertainty in capacity estimation at roundabouts

Object-Based and Supervised Detection of Potholes and Cracks From the Pavement Images Acquired by Uav

Contact Info

Product

Resources

About