Optimized minimal path selection (OMPS) method for automatic and unsupervised crack segmentation within two-dimensional pavement images

In many countries, the common practice for monitoring road surface conditions consists in collecting pavement images at traffic speed by devoted imaging devices. Among the surface distresses, cracking can serve as a condition indicator of the pavement structure. Image processing techniques have been then developed to computerize the survey of cracking as the support of human visual control. Among the existing automatic crack detection methods, the minimal path selection (MPS) technique has shown a better performance compared to other methods on simulated and field pavement images (Amhaz et al. in IEEE Trans Intell Transp Syst 17:2718-2729, 2016; Amhaz, in: Detection automatique de fissures dans des images de chaussee par selection de chemins minimaux, 2015). As a counterpart, MPS suffers from a large computing time. Within this scope, the aim of this paper has been to improve the efficiency of the original MPS method and to present more efficient strategies for the selection of minimal paths. Among the five main steps of the original MPS version, the improvements address the first three steps that enable the segmentation of the crack skeleton and reduce the computing burden on the last two steps. The tests of the two improved MPS versions on some image samples illustrate the large reduction in false positive paths without reducing the overall performance of the segmentation technique. Moreover, the computing time is divided by a factor sixty roughly for the latest MPS version.