Abstract
Abstract Pavement crack detection plays an important role in pavement maintaining and management. The three-dimensional (3D) pavement crack detection technique based on laser is a recent trend due to its ability of discriminating dark areas, which are not caused by pavement distress such as tire marks, oil spills and shadows. In the field of 3D pavement crack detection, the most important thing is the accurate extraction of cracks in individual pavement profile without destroying pavement profile. So after analyzing the pavement profile signal characteristics and the changeability of pavement crack characteristics, a new method based on the sparse representation is developed to decompose pavement profile signal into a summation of the mainly pavement profile and cracks. Based on the characteristics of the pavement profile signal and crack, the mixed dictionary is constructed with an over-complete exponential function and an over-complete trapezoidal membership function, and the signal is separated by learning in this mixed dictionary with a matching pursuit algorithm. Some experiments were conducted and promising results were obtained, showing that we can detect the pavement crack efficiently and achieve a good separation of crack from pavement profile without destroying pavement profile.
Highlights
In the life cycle of pavement, there will be various pavement distresses due to the burden of vehicles and natural causes
After analyzing the characteristics of main profile and cracking, we constructed a mixed over-complete dictionary according to main profile and crack characteristics and proposed a novel method based on sparse representation for crack detection and the mainly pavement profile extraction without noise
For our profile signal f contains crack signal sC and main profile signal sProf two layers as a linear combination, we propose to seek the sparsest of all representation over the mixed dictionary
Summary
In the life cycle of pavement, there will be various pavement distresses due to the burden of vehicles and natural causes. After analyzing the characteristics of main profile and cracking, we constructed a mixed over-complete dictionary according to main profile and crack characteristics and proposed a novel method based on sparse representation for crack detection and the mainly pavement profile extraction without noise.
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