Pavement Distress Estimation via Signal on Graph Processing

被引：6

作者：

Bruno, Salvatore ^{[1
]}

Colonnese, Stefania ^{[2
]}

Scarano, Gaetano ^{[2
]}

Del Serrone, Giulia ^{[1
]}

Loprencipe, Giuseppe ^{[1
]}

机构：

[1] Sapienza Univ Rome, Dept Civil, Construct & Environm Engn, Via Eudossiana 18, I-00184 Rome, Italy

[2] Sapienza Univ Rome, Dept Informat Engn, Elect & Telecommun, Via Eudossiana 18, I-00184 Rome, Italy

来源：

SENSORS | 2022年 / 22卷 / 23期

关键词：

pavement distress detection; pavement condition index; pavement management program; signal on graph processing; automated distress evaluation systems; Bayesian estimator; ASPHALT PAVEMENTS; IDENTIFICATION; POTHOLE;

D O I：

10.3390/s22239183

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

A comprehensive representation of the road pavement state of health is of great interest. In recent years, automated data collection and processing technology has been used for pavement inspection. In this paper, a new signal on graph (SoG) model of road pavement distresses is presented with the aim of improving automatic pavement distress detection systems. A novel nonlinear Bayesian estimator in recovering distress metrics is also derived. The performance of the methodology was evaluated on a large dataset of pavement distress values collected in field tests conducted in Kazakhstan. The application of the proposed methodology is effective in recovering acquisition errors, improving road failure detection. Moreover, the output of the Bayesian estimator can be used to identify sections where the measurement acquired by the 3D laser technology is unreliable. Therefore, the presented model could be used to schedule road section maintenance in a better way.

引用

页数：22

共 57 条

[41] Multichannel blind image deconvolution using the Bussgang algorithm: Spatial and multiresolution approaches
Panci, G
Campisi, P
Colonnese, S
Scarano, G
[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2003, 12 (11) : 1324 - 1337
[42] Development of network-level pavement deterioration curves using the linear empirical Bayes approach
Pantuso, Antonio
Flintsch, Gerardo W.
Katicha, Samer W.
Loprencipe, Giuseppe
[J]. INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2021, 22 (06) : 780 - 793
[43] Analysis of Pavement Condition Survey Data for Effective Implementation of a Network Level Pavement Management Program for Kazakhstan
Pantuso, Antonio
Loprencipe, Giuseppe
Bonin, Guido
Teltayev, Bagdat Burkhanbaiuly
[J]. SUSTAINABILITY, 2019, 11 (03):
[44] Poor H.V., 2013, INTRO SIGNAL DETECTI
[45] Queiroz C., 1992, ROAD INFRASTRUCTURE
[46] Pavement Distress Detection Methods: A Review
Ragnoli, Antonella
De Blasiis, Maria Rosaria
Di Benedetto, Alessandro
[J]. INFRASTRUCTURES, 2018, 3 (04)
[47] Identification of rut and pothole by using multirotor unmanned aerial vehicle (UAV)
Saad, Azri Mat
Tahar, Khairul Nizam
[J]. MEASUREMENT, 2019, 137 : 647 - 654
[48] Sahhaf S.A., 2014, P 8 NATL C CIVIL ENG
[49] Developing a near real-time road surface anomaly detection approach for road surface monitoring
Sattar, Shahram
Li, Songnian
Chapman, Michael
[J]. MEASUREMENT, 2021, 185
[50] Silyanov V. V., 2020, IOP Conference Series: Materials Science and Engineering, V832, DOI 10.1088/1757-899X/832/1/012056

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