Automatic Road Pavement Assessment with Image Processing: Review and Comparison

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

Road crack extraction with adapted filtering and Markov model-based segmentation : introduction and validation

International audienceThe automatic detection of road cracks is important in a lot of countries to quantify the quality of road surfaces and to determine the national roads that have to be improved. Many methods have been proposed to automatically detect the defects of road surface and, in particular, cracks: with tools of mathematical morphology, neuron networks or multiscale filter. These last methods are the most appropriate ones and our work concerns the validation of a wavelet decomposition which is used as the initialisation of a segmentation based on Markovian modelling. Nowadays, there is no tool to compare and to evaluate precisely the peformances and the advantages of all the existing methods and to qualify the efficiency of a method compared to the state of the art. In consequence, the aim of this work is to validate our method and to describe how to set the parametersLa détection automatique de fissures de chaussées est un tâche cruciale dans de nombreux pays. En effet, elle permet la qualité de la surface des chaussées dans le but de déterminer les améliorations nécessaires. De nombreuses méthodes ont été introduire pour réaliser la détection de défauts de surfaces et, en particulier, les fissures : en utilisant des outils de morphologie mathématiques, des réseaux de neurones ou un filtrage multi-échelle. Le filtrage multi-échelle semble le plus approprié et c'est la raison pour laquelle notre travail porte sur la validation d'une méthode à base de décomposition en ondelettes utilisée comme initialisation à une segmentation par modélisation markovienne. De nos jours, il n'existe pas d'outils permettant de comparer et d'évaluer précisément les performances et avantages des méthodes, d'une part, et de quantifier l'efficacité d'une méthode comparée à l'état de l'art. En conséquence, le deuxième objectif de ce travail est d'introduire un protocole d'évaluation afin de valider la méthode proposée et de décrire comment fixer les paramètres

Comparative study of steel corrosion characterization by visible and THz imaging techniques

International audienceTransport infrastructures play a significant role in the economy of countries. However, in European countries, transport infrastructures aging (>40 years) and traffic increase require to develop in-situ efficient inspection and maintenance solutions. Monitoring of steel and composite structures are important issues for sustainability of existing and new infrastructure. Classical approach relies on large human activities eventually performed in unsafe conditions. To overcome the problem on site contactless global automated measurement methods are to be favoured.For apparent corrosion, visible imaging coupled with image processing allows to detect and characterize the extension of the defective area. Anyway, characterization of corrosion thickness and nature require complementary measurements. Among imaging techniques, knowing that corrosion acts as a insulating layer, active infrared thermography is a possible approach [1-2]. But here we will focus on the complementary approach based on THz-TDS imaging as investigated and tested for corrosion detection under painting with preliminary corrosion type classification [2].In the present study, we first performed a measurement campaign on several steel samples at different corrosion stages. Typically, three stages were investigated: from non-corroded with paint coating, to pitting corrosion up to fully corroded sample surface.Data were gathered by means of the Z-Omega Fiber-Coupled Terahertz Time Domain (FICO) system working in a high-speed reflection mode and were processed by using a properly designed data processing chain recently proposed in [3] and involving a noise filtering procedure based on the Singular Value Decomposition (SVD) of the data matrix. Complementary post-processing approach for quick detection and characterization were added to these filtered data.The obtained results, which will be presented in detail at the conference, allowed us to state the imaging capabilities offered by the adopted instrumentation and obtain valuable information on the surveyed specimens, such as the corrosion thickness connection with apparent pseudo-intensity images. Finally, perspectives on coupling techniques will be introduced

Mesure de l'indice de microfissuration du béton par traitement d'images

Format électroniqueInternational audienc