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Research on morphological wavelet operator for crack detection of asphalt pavement
A novel, efficient image processing method is proposed here for extraction of pavement cracks from fuzzy and discontinuous asphalt pavement images. Pavement surface images obtained by CCD array, where pavement cracks are often blurry and discontinuous due to particle materials of pavement surface, crack degradation and unreliable crack shadows. Morphological wavelets are applied to crack detection of asphalt pavement. Proper morphological wavelet operator is here presented to decompose pavement images with cracks. Then, an appropriate decomposed image is selected, and cracks can be easily extracted through traditional binarization methods. Experiments show that the algorithm based on morphological wavelets is effective in extracting cracks of asphalt pavement, which is difficult to be detected through traditional algorithms.
Research on morphological wavelet operator for crack detection of asphalt pavement
A novel, efficient image processing method is proposed here for extraction of pavement cracks from fuzzy and discontinuous asphalt pavement images. Pavement surface images obtained by CCD array, where pavement cracks are often blurry and discontinuous due to particle materials of pavement surface, crack degradation and unreliable crack shadows. Morphological wavelets are applied to crack detection of asphalt pavement. Proper morphological wavelet operator is here presented to decompose pavement images with cracks. Then, an appropriate decomposed image is selected, and cracks can be easily extracted through traditional binarization methods. Experiments show that the algorithm based on morphological wavelets is effective in extracting cracks of asphalt pavement, which is difficult to be detected through traditional algorithms.
Research on morphological wavelet operator for crack detection of asphalt pavement
Wu, Guifang (author) / Sun, Xiuming (author) / Zhou, Lipeng (author) / Zhang, Haitao (author) / Pu, Jiexin (author)
2016-08-01
900817 byte
Conference paper
Electronic Resource
English