International audienc

In the field of noninvasive sensing techniques for civil infrastructures monitoring, this paper addresses the problem of crack detection, in the surface of the French national roads, by automatic analysis of optical images. The first contribution is a state of the art of the image-processing tools applied to civil engineering. The second contribution is about fine-defect detection in pavement surface. The approach is based on a multi-scale extraction and a Markovian segmentation. Third, an evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced. Finally, the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.

The state of roads is continuously degrading due to meteorological conditions, ground movements, and traffic, leading to the formation of defects, such as grabbing, holes, and cracks. In this article, a method to automatically distinguish images of road surfaces with defects from road surfaces without defects is presented. This method, based on supervised learning, is generic and may be applied to all type of defects present in those images. They typically present strong textural information with patterns that show fluctuations at small scales and some uniformity at larger scales. The textural information is described by applying a large set of linear and nonlinear filters. To select the most pertinent ones for the current application, a supervised learning based on AdaBoost is performed. The whole process is tested both on a textural recognition task based on the VisTex image database and on road images collected by a dedicated road imaging system. A comparison with a recent cracks detection algorithm from Oliveira and Correia demonstrates the proposed method's efficiency

The Thin-Plate Spline warp has been shown to be a very effective parameterized model of the optic flow field between images of various types of deformable surfaces, such as a paper sheet being bent. Recent work has also used such warps for images of a smooth and rigid surface. Standard Thin-Plate Spline warps are however not rigid, in the sense that they do not comply with the epipolar geometry. They are also intrinsically affine, in the sense of the affine camera model, since they are not able to simply model the effect of perspective projection.We propose three types of warps based on the Thin-Plate Spline. The first one is a rigid flexible warp. It describes the optic flow field induced by a smooth and rigid surface, and satisfies the affine epipolar geometry constraint. The second and third proposed warps extend the standard Thin-Plate Spline warp and the proposed rigid flexible warp to the perspective camera model. The properties of these warps are studied in details and a hierarchy is defined. Experimental results on simulated and real data are reported.

In the context of fine structure extraction, lots of methods have been introduced, and, particularly in pavement crack detection. We can distinguish approaches based on a threshold, employing mathematical morphology tools or neuron networks and, more recently, techniques with transformations, like wavelet decomposition. The goal of this paper is to introduce a 2D matched filter in order to define an adapted mother wavelet and, then, to use the result of this multi-scale detection into a Markov Random Field (MRF) process to segment fine structures of the image. Four major contributions are introduced. First, the crack signal is replaced by a more real one based on a Gaussian function which best represents the crack. Second, in order to be more realistic, i.e. to have a good representation of the crack signal, we use a 2D definition of the matched filter based on a 2D texture auto-correlation and a 2D crack signal. The third and fourth improvements concern the Markov network designed in order to allow cracks to be a set of connected segments with different size and position. For this part, the number of configurations of sites and potential functions of the MRF model are completed.

International audienceThis paper proposes a new algorithm for crack detection based on the selection of minimal paths. It takes account of both photometric and geometric characteristics and requires few information a priori. It is validated on synthetic and real images

In the context of computer vision, matching can be done using correlation measures. This paper presents the classification of fifty measures into five families. In addition, eighteen new measures based on robust statistics are presented to deal with the problem of occlusions. An evaluation protocol is proposed and the results show that robust measures (one of the five families), including the new measures, give the best results near occlusions.

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