The invention discloses a method for extracting
road surface disease features based on the sparse
decomposition theory and relates to the
road surface disease detection technique, which solves the problems that the contour
signal analysis technique based on
structured light has insufficient
feature extraction and unsatisfactory actual application effect and the like. The method comprises the following steps of: firstly, establishing different
disease feature atom dictionary according to different disease features, taking position and scale as parameters which change in a different ranges, and normalizing the atoms, thus obtaining a complete disease feature atom dictionary; secondly, according to the
signal spreading theory, selecting K atom pair signals from the complete disease feature atom dictionary to carry out K approximations, and then selecting an atom combination with a most sparse
decomposition coefficient from the K atom combinations according to the sparse
decomposition theory. The selection of the coefficient Ck of the disease feature should satisfy a
sparse constraint condition which is as follows:* C 0 s. t f =*Ck Phi k; the disease feature can be expressed as: f (t) = fk + Sigma = Sigma Ck Phi k (t)+ Sigma, wherein k = 0, 1, 2, and other integers; and Sigma is an approximate error. The method for extracting
road surface disease features is used for detecting road surface disease features, such as
crackles, tracing ruts, pits or earth bulges, and the like.