Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection

This paper proposes a new algorithm for automatic crack detection from 2D pavement images. It strongly relies on the localization of minimal paths within each image, a path being a series of neighboring pixels and its score being the sum of their intensities. The originality of the approach stems fr...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on intelligent transportation systems Ročník 17; číslo 10; s. 2718 - 2729
Hlavní autori: Amhaz, Rabih, Chambon, Sylvie, Idier, Jerome, Baltazart, Vincent
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.10.2016
Predmet:
Civil Engineering
Context
Crack detection
Dijkstra algorithm
Engineering Sciences
Image processing
Imaging
Materials
minimal path
non destructive control
road surface condition
Roads
Shape
Signal and Image processing
Surface cracks
Two dimensional displays
FISSURATION
DIJKSTRA
CHEMIN MINIMAL
CONTROLE NON DESTRUCTIF
CHAUSSEE
ALGORITHME
DETECTION
REVETEMENT (CHAUSSEE)
FISSURE
ISSN:1524-9050, 1558-0016
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This paper proposes a new algorithm for automatic crack detection from 2D pavement images. It strongly relies on the localization of minimal paths within each image, a path being a series of neighboring pixels and its score being the sum of their intensities. The originality of the approach stems from the proposed way to select a set of minimal paths and the two postprocessing steps introduced to improve the quality of the detection. Such an approach is a natural way to take account of both the photometric and geometric characteristics of pavement images. An intensive validation is performed on both synthetic and real images (from five different acquisition systems), with comparisons to five existing methods. The proposed algorithm provides very robust and precise results in a wide range of situations, in a fully unsupervised manner, which is beyond the current state of the art.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2015.2477675