Wavelet-Based Pavement Distress Image Edge Detection with À Trous Algorithm

Abstract

Edge detection is an alternative method in the process for identifying and classifying pavement cracks for automated pavement evaluation systems. A number of edge detectors are widely used in image processing; most specify only a spatial scale for detecting edges. However, pavement surface images frequently have various details at different scales. Therefore, wavelet-based multiscale technique can be a candidate to extract edge information from pavement surface images. Instead of detecting edges in the space domain, wavelet analysis has the ability to describe both domains in time and in frequency. It was first applied in image edge detection in 1992, using the local maximum of the magnitude of the gradient to obtain edge representation. Nevertheless, this subsampling algorithm leads to a loss of translation variance and may produce many artifacts. In this paper, wavelet edge detection based on à trous algorithm (holes algorithm) is used in pavement distress segmentation. This algorithm is an undecimated wavelet transform executed via a filter bank without subsampling process. Translation invariance is one of its most important advantages. Therefore, the algorithm can minimize the artifact in the denoised data. Results of experiments on images are discussed in the paper. By comparisons with the results derived from five other traditional edge detectors, the study demonstrates the validity and effectiveness of this method for edge detection of pavement surface distresses.