Efficient pavement crack detection and classification

Each year, millions of dollars are invested on road maintenance and reparation all over the world. In order to minimize costs, one of the main aspects is the early detection of those flaws. Different types of cracks require different types of repairs; therefore, not only a crack detection is require...

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Bibliographic Details
Published in:EURASIP journal on image and video processing 2017-06, Vol.2017 (1), p.1-11, Article 39
Main Authors: Cubero-Fernandez, A., Rodriguez-Lozano, Fco. J., Villatoro, Rafael, Olivares, Joaquin, Palomares, Jose M.
Format: Article
Language:English
Subjects:
Algorithms
Automatic detection
Biometrics
Classification
Crack detection
Cracks
Defects
Engineering
Heuristic classifier
Heuristic methods
Image Processing and Computer Vision
Nondestructive testing
Pattern Recognition
Pavement crack
Preprocessing
Road maintenance
Road safety
Signal,Image and Speech Processing
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Summary:Each year, millions of dollars are invested on road maintenance and reparation all over the world. In order to minimize costs, one of the main aspects is the early detection of those flaws. Different types of cracks require different types of repairs; therefore, not only a crack detection is required but a crack type classification. Also, the earlier the crack is detected, the cheaper the reparation is. Once the images are captured, several processes are applied in order to extract the main characteristics for emphasizing the cracks (logarithmic transformation, bilateral filter, Canny algorithm, and a morphological filter). After image preprocessing, a decision tree heuristic algorithm is applied to finally classify the image. This work obtained an average of 88% of success detecting cracks and an 80% of success detecting the type of the crack. It could be implemented in a vehicle traveling as fast as 130 kmh or 81 mph.
ISSN:1687-5281
1687-5176
1687-5281
DOI:10.1186/s13640-017-0187-0