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Dans la contexte de l'extraction de structure fine, ce papier présente une nouvelle méthode s'appuyant sur une segmentation multi-résolution appliquée au cas des images routières. Une méthode initialement développée pour la détection de membranes biologiques faiblement contrastées a été adaptée aux cas de la détection de fissures dans des images. E...
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... average contrast C seg of a segment is the difference between the aver- age gray-level of the segment (GL seg ), and the average gray-level of the neighboring background (GL Bkg ): GL Bkg (e.g. Figure 5) is measured using a large filter averaging the neighboring values of the background (pixels not labeled as PCP). The filter kernel size did not seem critical, but it should be large enough to re- duce the influence of noise, and small enough to con- sider the irregularities of the road: a 25 × 25 pixels kernel was experimentally chosen. ...
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Citations
... There are several studies in the literature on crack detection [8,9,11,12,[16][17][18], but regarding cracking in ceramics there are few works and each surface has its specificity. ...
Cracks are pathologies whose appearance in ceramic tiles can cause various types of scratches due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time to map the entire area and high cost. These inspections require special equipment when they are at high altitudes, and the integrity of the inspector is at risk. Thus, there exists a need for automated optical inspection to find faults in ceramic tiles. This work focuses on the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes a pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The results show that the proposed architecture for ceramic crack segmentation achieves promising performance
... There are several studies in the literature on crack detection [8,9,11,12,[16][17][18], but regarding cracking in ceramics there are few works and each surface has its specificity. ...
Cracks are pathologies whose appearance in ceramic tiles can cause various damages due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for addressing this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time and a high cost to map the entire area. This work focuses on automated optical inspection to find faults in ceramic tiles performing the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes an image pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The proposed model can adequately identify the crack even when it is close to or within the grout.
... However, the automatic implementation of these methods remains difficult because of the large amount of parameters to tune. Based on the fact that cracks can have different width and size [16], multi-scale analysis [17,18], wavelet decomposition [19] have been intensively used, but the main difficulty is to select the right scale for identifying cracks. ...
Pavement condition information is a significant component in pavement management systems. Precise extraction of road degradations particularly cracks is a critical task for surface safety. Manual surveys, which are labor intensive and costly, have induced several researchers to investigate the use of image processing to achieve automated pavement distress ratings. In the context of fine structures extraction, we present in this paper a novel approach for road crack detection under real conditions using several systems installed differently on a vehicle. It is such an automatic and effective approach that relies on both photometric and geometric characteristics of cracks. Based on an edge detection technique to avoid the bad conditions of image acquisition and an examination algorithm to verify the presence of high concentration of cracking pixels, this approach allows in a first step to select pixels that have great probability of belonging to a crack. Indeed, the originality of this approach stems from the proposed way to compute a set of thin filaments connecting the pixels selected at the first step between them. Finally, a post-processing step is applied to refine the obtained result and confirm either the presence or the absence of cracks in the image. Our proposed approach provides very robust and precise results on 2D pavement images in a wide range of situations and in a fully unsupervised manner. Furthermore, its innovative aspect is reflected in its ability to analyze easily both 2D and 3D pavement images.
... Geometry constraints-based methods extract information from topology data. Many methods are based on multi-scale analysis for fine structure extraction, while some employ watershed method [13] and wavelet decomposition [14]. ...
... 14: if (p(x, y) (subImage ij ) > 0) then 15: p(x, y) ( if (p(x, y) (S) >μS) then μS denotes arithmetic mean of the image S where the value of a pixel is less than 255. When is greater then σ ij and p(x, y) subImageij is greater then 0 then the pixel is marked as background pixel (lines [11][12][13][14][15][16][17][18][19]. The remaining pixels are considered as pavement crack pixels. ...
... There are various methods based on multi-scale analysis for the fine structure extraction. Two examples are based on the watershed method [24] and the wavelet decomposition [15]. ...
Pavement cracks are the first signs of structural damage in the asphalt pavement surfaces. The oldest method for detection and estimation of the pavement cracks is human visual
inspection, also known as manual visual inspection. However, using human inspectors is very time consuming, very expensive and poses a risk to human safety. Another negative side is the fact that the task generally requires road to be closed. Hence, automatic prevention and reparation of cracks on the asphalt surface pavements is an important task, especially because the advanced stages of road deformation lead to formation of
potholes. This has negative impact on the total reparation cost. In this paper, we proposed a new unsupervised method for the detection of cracks with gray color based histogram and Ostu’s thresholding method on 2D pavement image. At first, the method divides the input image into a four independent equally sized sub-images. Then, the search for cracks is based on the ratio between Ostu’s threshold and the maximum histogram value for
every sub-image. Finally, all sub-images are assembled into the resulting image. The method was tested on the dataset which contains different pavement images with very versatile types of cracks. The results showed that the proposed method achieves satisfactory performance, especially in the cases of low signal-to-noise ratio, and is very fast.
... In material context, there are many works that focus on cracks in roads or concrete. Differ- ent tools are used as statistical ones (co-occurence matrices in [15] and anisotropic diffusion with region linkage in [17]), wavelets [23], geodesic contours [6], multiscale approach [7] together with Markov modeling or image-based percolation models [26] and dealing with brightness and connectivity as in [16]. ...
The goal of this paper is the presentation of a post-processing method allowing to remove impulse noise in binary images, while preserving thin structures. We use a grain filter. We propose a method to automatically determine the required threshold using Galton-Watson processes. We present numerical results and a complete analysis on a synthetic image. We end the numerical section considering a specific application to granite samples crack detection: here we deal with X-tomography images that have been binarized via preprocessing techniques and we want to remove residual impulse noise while keeping cracks and micro-cracks structure.
... In addition, algorithms have been proposed for the detection of road cracks using road imagery and image processing. These algorithms are dependent on parameters such as acquisition, storage and image processing (Scheffy and Diaz 1999, Chambon and Moliard 2011, Georgopoulos et al. 2006, Corso et al. 1995, Tanaka and Uematsu 1998, Rughooputh et al. 2000, Mahler et al.1991, Iyer and Sinha 2005, Oliveira and Correia 2009,Hsu et al. 2001, Augereau et al. 2001, Chambon 2011, Coudray et al. 2010). ...
The Pavement networks require a systematic method to control the
Maintenance and Rehabilitation (M&R) process, to define priorities and ensure
optimum allocation of resources. A Pavement Maintenance Management System
(PMMS) is a useful tool for evaluation, prioritization of M&R projects, and
determination of allocation and funding requirements. This research work used a
novel architecture of a PMMS system; a Wireless Sensor Network (WSN) with
image processing to identify a particular pavement distress namely alligator
cracking. An automated analysis provides a tool to accurately analyze and
classify pavements within a predefined category in order to take adequate
measures. Data sets for image processing are collected from typical areas in the
pavement network. These data are analyzed to produce the pavement condition
index (PCI). PCI is a numerical measure that evaluates the surface condition of
the pavement. It provides an indicator of the present pavement condition based
on the distress level measured on the surface of the pavement. The novel
architecture is proposed for real time data collection and transmission to a remote
central processing management system using a mobile network. An image
processing alligator cracks detection algorithm along with data fusion are
presented within the WSN architecture. Alligator cracking was chosen because it
is a common distress and purely load (structural) related.
... The application of automated surveys based on a variety of electronic sensors (e.g., video cameras and laser sensors) became common in the 1980s (Curphey et al., 1985;Haas et al., 1985;. These various types of sensors are designed to detect and assess either a specific type of individual distress such as transverse cracks or a specific type of pavement such as concrete Mahler et al., 1991;Georgopoulos et al., 1995;Pynn et al., 1999;Lee and Kim, 2005;Huang and Xu, 2006;Zhou et al., 2006;Oliveira and Correia, 2008;Nguyen et al., 2009;Coudray et al., 2010;Gavilan et al., 2011;Koch and Brilakis, 2011;Adarkwa and Attoh-Okine, 2013). ...
State transportation agencies regularly collect data on pave-ment surface distresses. These data are used to assess overall pavement conditions and to make maintenance and repair de-cisions. Routinely-acquired and publically-available high spa-tial resolution (HSR) multispectral digital aerial photography provides a potential method for collecting distress information that can supplement or replace currently-used technologies. Principal component analysis and linear least squares regres-sion models were used to evaluate the potential of using HSR multispectral digital aerial photographs to estimate pavement surface overall distress conditions. Various models were de-veloped using HSR multispectral digital aerial photographs of different spatial resolution (6-inch, 12-inch, and 24-inch) and reference pavement surface distress data collected manually at multiple sample sites using standard protocols. The results show that the spectral response of HSR multispectral digital aerial photographs correlate strongly with reference distress rates at all tested spatial resolutions, but the 6-inch aerial photos exhibit the strongest correlation (R2 > 0.95), even when using only half of the sample sites (R2 > 0.92). These results in-dicate that straightforward analysis of HSR multispectral digital aerial photographs, routinely acquired by most municipalities and states, can permit assessment of pavement surface distress conditions as well as current manual evaluation protocols.
... However, the automatic implementation of the latter methods remains difficult because of the large amount of parameters to tune. Based on the fact that cracks can have different width and size, multi-scale analysis with watersheds [20], wavelet decomposition [21], [22] have been intensively used but the main difficulty is to select the right scale for identifying cracks or how to combine the detections at multi-scale to compute the final decision. ...
This paper proposes a new algorithm for automatic crack detection from pavement images. It heavily relies on the localization of minimal paths within each image, a path being a series of neighbouring pixels with low intensities. The originality of the approach is about the manner to select the set of minimal paths and the two post-processing steps introduced based on these minimal paths. Such an approach is a natural way to take account of both photometric and geometric characteristics of pavement images. The resulting crack detection method incorporates very few parameters. An intensive validation on both synthetic and real images is provided, with comparisons to existing methods.
... Selon S. Chambon [2,39], cette modélisation ne correspond pas à une représentation réaliste de la fissure. Elle a donc choisi la modélisation gaussienne (relation (1.4)). ...
These last decades have seen application of automatic inspection in many fields thanks to advanced vision sensors and image analysis methods. However, the difficult nature of pavement images, the small size of defects (cracks) lead to the fact that inspection in this area is done mostly manually. Each year in France, operator must view images of thousands kilometers of roads to detect these degradations. This method is expensive, slow and has a rather subjective result. The objective of this thesis is to develop a method for the detection and the classification of cracks on these pavement images automatically. In this thesis, a new method of segmentation has been developed: the Free Form Anisotropy (FFA). On one hand, this method allows to take into account both the features concerning form and intensity of cracks, for the detection. On the other hand, a new model is used to search minimum paths in graphs (images). This minimum path follows crack form when crack is present. After segmentation, extraction and classification of defects are performed by the Standard Hough Transform and by calculating local orientation of pixels. Experimental results have been obtained from different image databases and compared with other existing methods.
