FoSA: F* Seed-growing Approach for crack-line detection from pavement images

Most existing approaches for pavement crack line detection implicitly assume that pavement cracks in images are with high contrast and good continuity. This assumption does not hold in pavement distress detection practice, where pavement cracks are often blurry and discontinuous due to particle materials of road surface, crack degradation, and unreliable crack shadows. To this end, we propose in this paper FoSA — F* Seed-growing Approach for automatic crack-line detection, which extends the F* algorithm in two aspects. It exploits a seed-growing strategy to remove the requirement that the start and end points should be set in advance. Moreover, it narrows the global searching space to the interested local space to improve its efficiency. Empirical study demonstrates the correctness, completeness and efficiency of FoSA.

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► FoSA is a fully-automatic approach for detecting cracks from pavement images.
► FoSA formulates the crack-detection problem into a seed-growing problem.
► FoS achieves the seed growing by searching a minimum cost path.
► FoSA outperforms the traditional approaches in pavement crack detection.