Stéphane Begot

Free-form anisotropy: A new method for crack detection on pavement surface images

This paper presents a new measure which takes into accounts simultaneously brightness and connectivity, in the segmentation step, for crack detection on road pavement images. Features which are calculated along every free-form paths provide detection of cracks with any form and any orientation. The method proposed does not need learning stage of free defect texture to perform default detection. Experimental results were conducted on some samples of different kinds of pavements. Results of the method are also given on other kinds of images and can provide perspectives on other domains as road extraction on satellite images or segment blood vessels in retinal images.

2D image based road pavement crack detection by calculating minimal paths and dynamic programming

Road distress needs to be detected early to optimize road maintenance cost; automatic survey of road distress is a big challenge, particularity for the detection of tiny cracks due to important variation of pavement textures. This paper presents a new method for crack detection by finding the minimal path passing on each pixel of image from every path with a length d; we propose also a dynamic programming implementation to make it applicable in real condition. Methods are tested on synthesis images set and a large set of real images. Results show that cracks as small as 2mm could be detected.

Road markings detection and tracking using hough transform and kalman filter

A lane marking tracking method using Hough Transform and Kalman Filtering is presented. Since the HT is a global feature extraction algorithm, it leads to a robust detection relative to noise or partial occlusion. The Kalman filter is used to track the roadsides which are detected in the image by this HT. The Kalman prediction step leads to predict the road marking parameters in the next frame, so we can apply the detection algorithm in smaller regions of interest, the computional cost is being consequently reduced.

PAVEMENT CRACKING DETECTION USING AN ANISOTROPY MEASUREMENT

Automatic pavement cracking detection is a part of road maintenance and rehabilitation strategies. Cracks detection is one of the main features used by road authorities to manage efficiently its networks. Different systems are available to perform road analysis. We give a short description of some of them. Apparatus which was used to provide our images is described with more details. Road surface is made using randomly organized aggregates which can have different sizes. Scanned pictures of theses surfaces appear as random distribution of a reduced set of gray levels. Automatic crack detection is a difficult task due to the noisy pavement surface. In this paper, we introduce a measure of anisotropy for the characterization of cracks. The basic idea of this method is to detect the variation of features by considering different orientations. Noise variation and defect properties can be take into account by our method. Comparative results of anisotropy method with threshold method and 2D wavelet transform method are presented to illustrate benefits of anisotropy. We show that this method can be used to detect others types of defects, such as joints.

Hidden Markov model framework for industrial maintenance activities

This article deals with modelization of industrial process by using hidden Markov model. The process is seen as a discrete event system. We propose different structures based on Markov automata, called topologies. A synthetic hidden Markov model is designed in order to match to a real industrial process. The models are intended to decode industrial maintenance observations (also called “symbol”). Symbols are produced with a corresponding degradation level (also called “state”). These 2-tuple (symbol, state) are known as Markov chains, also called “a signature.” Hence, these various 2-tuple are implemented in the proposed topologies by using the Baum–Welch learning algorithm (decoding by forward variable) and the segmental K-means learning (decoding by Viterbi). We assess different frameworks (topology, learning and decoding algorithm, distribution) by relevancy measurements on model outputs. Then, we determine the most relevant framework for use in maintenance activities. Afterward, we try to minimize the size of the learning data. Thus, we could evaluate the model by using “sliding windows” of data. Finally, an industrial application is developed and compared with this framework. Our goal is to improve worker safety, maintenance policy, process reliability and reduce CO2 emissions in the industrial sector.