Geometrical flow‐guided fast beamlet transform for crack detection
Open Access
- 1 March 2018
- journal article
- research article
- Published by Institution of Engineering and Technology (IET) in IET Image Processing
- Vol. 12 (3), 382-388
- https://doi.org/10.1049/iet-ipr.2017.0747
Abstract
Beamlet transform has been widely used for extracting line features from images, which is an excellent multiscale geometric analysis method. However, it has a major drawback that it always performs too slowly due to very much redundant computation. In many application fields, the speed of the original beamlet transform is almost unbearable. To cure the problem, beamlet transform is improved by introducing geometrical flow, which utilises image semantic information in the process of generating beamlets. Besides, to further speed up the algorithm, interesting factor is presented to reduce recursively partitioned boxes. As a result, lots of computation time is saved. Experiments are conducted on various crack images and the results show that the proposed method runs significantly faster than the original beamlet transform. Cracks in an image are detected accurately. Moreover, the proposed method is robustly enough since the performance is hardly affected by crack shape and background texture.Keywords
Funding Information
- National Natural Science Foundation of China (10972126, 11472162)
This publication has 21 references indexed in Scilit:
- Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path SelectionIEEE Transactions on Intelligent Transportation Systems, 2016
- Automatic Road Crack Detection Using Random Structured ForestsIEEE Transactions on Intelligent Transportation Systems, 2016
- Visual analytics and rendering for tunnel crack analysisThe Visual Computer, 2016
- CrackTree: Automatic crack detection from pavement imagesPattern Recognition Letters, 2012
- Adaptive Road Crack Detection System by Pavement ClassificationSensors, 2011
- Micro crack detection with Dijkstra’s shortest path algorithmMachine Vision and Applications, 2011
- A robust automatic crack detection method from noisy concrete surfacesMachine Vision and Applications, 2010
- Image‐Based Crack Detection for Real Concrete SurfacesIEEJ Transactions on Electrical and Electronic Engineering, 2007
- Segmentation of buried concrete pipe imagesAutomation in Construction, 2006
- Automated detection of cracks in buried concrete pipe imagesAutomation in Construction, 2006