Abstract
Asphalt pavement distress is very important for road maintenance and rehabilitation decisions. The traditional manual pavement crack detection by human eyes is expensive, labor intensive, time consuming, and subjective. Automatic pavement distress detection algorithms are developed quickly in recent years. Segmentation is one of important step in automated pavement crack detect system. In this paper, a new segmentation algorithm by multi-scale and local optimum threshold is developed. The algorithm was shown to be more effective and robust than conventional segmentation algorithms.
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Wang, S., Tang, W. (2011). Pavement Crack Segmentation Algorithm Based on Local Optimal Threshold of Cracks Density Distribution. In: Huang, DS., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing. ICIC 2011. Lecture Notes in Computer Science, vol 6838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24728-6_40
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DOI: https://doi.org/10.1007/978-3-642-24728-6_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24727-9
Online ISBN: 978-3-642-24728-6
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