Road maintenance management presents a complex task for road authorities. The first presumption for the evaluation analysis and correct road construction rehabilitation is to have precise and up-to-date information about road pavement condition and level degradation. Different road crack types were proposed in the state of art in order to provide useful information for making pavement maintenance strategies. For this reason, we present in this paper a novel research to automatically detect and classify road cracks on two-dimensional digital images. Indeed, our proposed package is composed of two methods: crack detection and crack classification. The first method consists in detecting the cracks on images acquired by the VIAPIX® system developed by our company ACTRIS. To do so, we are based on our unsupervised approach cited in for road crack detection on two-dimensional pavement images. Then, in order to categorize each of the detected cracks, the second method of our package is applied. Based on principal component analysis (PCA), our method permits the classification of all the detected cracks into three types: vertical, horizontal, and oblique. The obtained results demonstrate the efficiency of our robust approaches in terms of good detection and classification on a variety of pavement images.
In this paper we present a novel approach for road sign identification and geolocation based on Joint Transform Correlator “JTC” and VIAPIX module. The proposed method is divided into three parts: identification, gathering and geolocation. The first part permits to detect and identify road signs on images acquired by the VIAPIX module  developed by our company ACTRIS . To do so, we are based on our own method cited in  for road sign identification. The second part of our proposed approach consists in gathering the identified road sign by using the JTC technique . Since the VIAPIX® module provides images at an interval of one image per meter, we identify each road sign by finding the number of images where this road sign has been recognized while computing thereby on each of these images its corresponding pixel coordinates. Finally, each road sign is geolocated using its pixel coordinates on several images. At this stage, we are based on the axial stereovision method . Indeed, relying on the pixel coordinates and the distance between different images, we compute the 3D coordinates of each road sign. Thus, GPS coordinates can be then found using the GPS position of the vehicle basing on Vincenty formulae .