Automated Crack Detection on Concrete Bridges

• Published in: IEEE transactions on automation science and engineering 2016-04, Vol.13 (2), p.591-599
• Main Authors: Prasanna, Prateek, Dana, Kristin J., Gucunski, Nenad, Basily, Basily B., La, Hung M., Lim, Ronny Salim, Parvardeh, Hooman
• Format: Article
• Language: English
• Subjects: Adaboost; bridge deck inspection; bridge maintenance; Bridges; computer vision; Concrete; crack detection; crack pattern recognition; Cracks; Data collection; homography; image mosaic; Image segmentation; image stitching; Laplace equations; Laplacian pyramid; machine learning; random forest; robotic imaging; robotic inspection; Robotics; Robots; Robustness; Seekur robot; Structural engineering; structural health monitoring; structure from motion; STRUM classifier; support vector machine; Visualization
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2014.2354314

Detection of cracks on bridge decks is a vital task for maintaining the structural health and reliability of concrete bridges. Robotic imaging can be used to obtain bridge surface image sets for automated on-site analysis. We present a novel automated crack detection algorithm, the STRUM (spatially tuned robust multifeature) classifier, and demonstrate results on real bridge data using a state-of-the-art robotic bridge scanning system. By using machine learning classification, we eliminate the need for manually tuning threshold parameters. The algorithm uses robust curve fitting to spatially localize potential crack regions even in the presence of noise. Multiple visual features that are spatially tuned to these regions are computed. Feature computation includes examining the scale-space of the local feature in order to represent the information and the unknown salient scale of the crack. The classification results are obtained with real bridge data from hundreds of crack regions over two bridges. This comprehensive analysis shows a peak STRUM classifier performance of 95% compared with 69% accuracy from a more typical image-based approach. In order to create a composite global view of a large bridge span, an image sequence from the robot is aligned computationally to create a continuous mosaic. A crack density map for the bridge mosaic provides a computational description as well as a global view of the spatial patterns of bridge deck cracking. The bridges surveyed for data collection and testing include Long-Term Bridge Performance program's (LTBP) pilot project bridges at Haymarket, VA, USA, and Sacramento, CA, USA.