Automatic Road Crack Segmentation Using Entropy and Image Dynamic Thresholding
Automatic Road Crack Segmentation Using Entropy and Image Dynamic Thresholding, Proc European Signal Processing Conf. - EUSIPCO, Glasgow, United Kingdom, Vol. -, pp. - - -, August, 2009.
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Human observation is commonly used to collect pavement surface distress data, during periodic road surveys. This method is labour-intensive, subjective and potentially haz-ardous for both inspectors and road users. This paper pre-sents a novel framework for automatic crack detection and classification using survey images acquired at high driving speeds. The resulting images are pre-processed using mor-phological filters for reducing pixel intensity variance. Then, a dynamic thresholding is applied to identify dark pixels in images, as these correspond to potential crack pixels. Thresholded images are divided into non-overlapping blocks for entropy computation. A second dynamic thresholding is applied to the resulting entropy blocks matrix, used as the basis for identification of image blocks containing crack pix-els. The classification system then labels images as contain-ing horizontal, vertical, miscellaneous or no cracks. Two image databases are used for test purposes, to infer about the method’s robustness, one of which acquired using profes-sional high speed equipment