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Robust Automated Concrete Damage Detection Algorithms for Field Applications.
- Source :
- Journal of Computing in Civil Engineering; Mar2014, Vol. 28 Issue 2, p253-262, 10p, 4 Color Photographs, 2 Black and White Photographs, 1 Diagram, 7 Charts, 3 Graphs
- Publication Year :
- 2014
-
Abstract
- This paper presents a computer vision framework supporting automated infrastructure damage detection, with a specific focus on surface crack detection in concrete. The approach presented is designed to provide a significant increase in robustness relative to existing methods when faced with widely varying field conditions while operating fast enough to be used in large scale applications. In particular, a clustering method for segmentation is developed that exploits inherent characteristics of fracture images to achieve consistent performance, combined with robust feature extraction to improve recognition algorithm classifier outcomes. The approach is shown to perform well in detecting cracks across a broad range of surface and lighting conditions, which can cause existing techniques to exhibit significant reductions in detection accuracy and/or detection speed. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
COMPUTER vision
SURFACE cracks
ACCURACY
AUTOMATION
Subjects
Details
- Language :
- English
- ISSN :
- 08873801
- Volume :
- 28
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of Computing in Civil Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 94449574
- Full Text :
- https://doi.org/10.1061/(ASCE)CP.1943-5487.0000257