Automatic mapping of cracking patterns on concrete surfaces with biological stains using hyper-spectral images processing

摘要

Despite all technological advances, mapping cracks on concrete structures mostly remains to be evaluated through sketches based on on-site observation and photographs. Methods based on image processing have been developed with clear advantages. However, most studies rely on perfectly identified areas or on single cracks without any other pathologies, being therefore unsuitable for on-site application. In addition, the accuracy is not usually quantified due to the absence of ground-truth. Thus, methods for automatic mapping of cracking patterns, sufficiently robust to deal with the surrounding pathologies, are of great interest. The Super Cluster-Crack method (SC-Crack method) is herein presented. It was developed for crack detection in concrete surfaces, with biological stains, by processing hyperspectral images. SC-Crack performs k-means clustering, followed by grouping clusters to composing a super cluster that stands for the cracks. The method was calibrated and validated by classifying hyperspectral images of concrete specimens, within bandwidths of 25nm in a wavelength range between 425nm and 950nm. Results are discussed by comparison with the ground-truth image. Finally, the super cluster composition is also validated. The SC-Crack method performs successfully both on clean and on surface with biological stains. In the latter case, hyperspectral images help to avoid mixing biological stains with crack pattern. Concerning the main goal of mapping the cracking pattern, the method performs perfectly on concrete clean surfaces, allowing to detect all the crack branches. In the case of surface with biological stains, the SC-Crack also detects the majority of cracking pattern, except for the thinner branches.