A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

被引：747

作者：

Koch, Christian ^{[1
]}

Georgieva, Kristina ^{[2
]}

Kasireddy, Varun ^{[3
]}

Akinci, Burcu ^{[3
]}

Fieguth, Paul ^{[4
]}

机构：

[1] Univ Nottingham, Dept Civil Engn, Nottingham NG7 2RD, England

[2] Ruhr Univ Bochum, Chair Comp Engn, D-44801 Bochum, Germany

[3] Carnegie Mellon Univ, Dept Civil & Environm Engn, Pittsburgh, PA 15213 USA

[4] Univ Waterloo, Fac Engn, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada

来源：

ADVANCED ENGINEERING INFORMATICS | 2015年 / 29卷 / 02期

关键词：

Computer vision; Infrastructure; Condition assessment; Defect detection; Infrastructure monitoring; OF-THE-ART; CRACK DETECTION; SCENE RECONSTRUCTION; MACHINE VISION; INSPECTION; SEGMENTATION; SYSTEM; CLASSIFICATION; DETERIORATION; RETRIEVAL;

D O I：

10.1016/j.aei.2015.01.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：196 / 210

页数：15

共 119 条

[31]

Federal Highway Administration (FHWA) and Federal Transit Admistration, 2005, HIGHW RAIL TRANS TUN

[32]

Federal Highway Admistration (FHWA), 2011, TUNN OP MAINT INSP E

[33]

Fujita Y, 2006, INT C PATT RECOG, P901

[34]

German Department of Transportation - Road and Transportation Research Association (FGSV), 2006, ADD TECHN CONTR TERM

[35] Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments [J].

German, Stephanie ;

Brilakis, Ioannis ;

DesRoches, Reginald .

ADVANCED ENGINEERING INFORMATICS, 2012, 26 (04) :846-858

[36]

Guo W., 2009, AUTOM CONSTRUCT, V18, P87

[37]

Haas C., 1996, Automation in Construction, V4, P293, DOI 10.1016/0926-5805(95)00010-0

[38] Expert system for prioritizing the inspection of sewers: Knowledge base formulation and evaluation [J].

Hahn, MA ;

Palmer, RN ;

Merrill, MS ;

Lukas, AB .

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE, 2002, 128 (02) :121-129

[39] Efficient Algorithm for Crack Detection in Sewer Images from Closed-Circuit Television Inspections [J].

Halfawy, Mahmoud R. ;

Hengmeechai, Jantira .

JOURNAL OF INFRASTRUCTURE SYSTEMS, 2014, 20 (02)

[40] Optical flow techniques for estimation of camera motion parameters in sewer closed circuit television inspection videos [J].

Halfawy, Mahmoud R. ;

Hengmeechai, Jantira .

AUTOMATION IN CONSTRUCTION, 2014, 38 :39-45

← 1 2 3 4 5 6 7 8 9 10 →