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

Authors:
Qingquan Li;Qin Zou;Daqiang Zhang;Qingzhou Mao
Affiliations:
Transportation Research Center, Wuhan University, Wuhan 430079, China and School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;Transportation Research Center, Wuhan University, Wuhan 430079, China and School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;School of Computer Science, Nanjing Normal University, Nanjing 210097, China;Transportation Research Center, Wuhan University, Wuhan 430079, China
Venue:
Image and Vision Computing
Year:
2011

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Abstract

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.