Browsing by Author "Mery, D."

Now showing 1 - 8 of 8
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    A survey of land mine detection technology
    (TAYLOR & FRANCIS LTD, 2009) Robledo, L.; Carrasco, M.; Mery, D.
    This paper describes the state of the art in land mine detection technology and algorithms. Landmine detection is a growing concern due to the danger of buried landmines to people's lives, economic growth and development. Most of the injured people have no connection with the reason why the mines were placed. There are 50-100 million landmines in more than 80 countries around the world. Deactivation is estimated at 100 000 mines per year, against the nearly 2 million mines laid annually. In this paper we describe and analyse sensor technology available including state-of-the-art technology such as ground penetrating radar (GPR), electromagnetic induction (EMI) and nuclear quadrupole resonance (NQR) among others. Robotics, data processing and algorithms are mentioned, considering support vectors, sensor fusion, neural networks, etc. Finally, we establish conclusions highlighting the need to improve not only the way images are acquired, but the way this information is processed and compared.
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    Active X-ray testing of complex objects
    (BRITISH INST NON-DESTRUCTIVE TESTING, 2012) Riffo, V.; Mery, D.
    X-ray testing of complex objects - such as luggage screening at airports - is usually performed manually. This is not always effective, since it depends strongly on the pose of the objects of interest (target objects) and occlusion, as well as human capabilities. Additionally, certain target objects are difficult to detect using only one viewpoint. For this reason, we have developed an active X-ray testing framework that is able to find an adequate viewpoint of the target object in order to obtain better X-ray images to analyse. The key idea of our method is to adapt automatically the viewpoint of the X-ray images in order to project the target object in poses where the detection performance should be higher. Thus, the detection inside complex objects can be performed in a more effective way. Using a robotic arm and a semiautomatic manipulator system, the robustness and reliability of the method have been verified in the automated detection of razor blades located inside nine different objects, showing promising preliminary results: in 130 experiments we were able to detect the razor blade 115 times with 10 false alarms, achieving a recall of 89% and a precision of 92%.
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    Automated radioscopic testing of aluminum die castings
    (AMER SOC NONDESTRUCTIVE TEST, 2006) Mery, D.
    Castings produced for the automotive industry are considered important components for overall roadworthiness. To ensure the safety of construction, it is necessary to check every part thoroughly using nondestructive testing (NDT). Radioscopy rapidly became the accepted way for controlling the quality of die cast pieces. hi this paper, the fundamental principles of the automated detection of casting discontinuities are explained. A general automated testing schema is presented and several techniques that have appeared in the literature in the past 20 years are explained, showing the development of this sector in the areas of industry and academia. Finally, advances in the simulation of discontinuities, used for assessing the performance of a test technique, are outlined.
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    Automated testing of aluminum castings using classifier fusion strategies
    (AMER SOC NONDESTRUCTIVE TEST, 2005) Mery, D.; Chacon, M.; Gonzalez, L.; Munoz, L.
    Generally, discontinuity detection in automated visual testing consists of two steps: identification of potential discontinuities using image processing techniques and classification of potential discontinuities into discontinuities and regular structures (false alarms) using a pattern recognition methodology. In the second step, since several features cyan be extracted from the potential discontinuities, a feature selection must be performed. In this paper, several known classifiers are studied in automated visual testing: threshold, euclidean, mahalanobis, polynomial, support vector machine and neural network classifiers. First, the performance of the classifiers is assessed individually. Second, the classifiers are combined in order to improve their performance. Seven fusion strategies in the combination were tested: and, or, majority vote, product, sum, maximum and median. The proposed methodology was tested on real data acquired from 50 noisy radiographic images of aluminum wheels, where 23 000 potential discontinuities (with only 60 real discontinuities) were segmented and 405 features were extracted for each potential discontinuity. Using fusion of classifiers, a very good performance was achieved, yielding a sensitivity of 100% and specificity of 99.97%.
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    Flaw detection in aluminium die castings using simultaneous combination of multiple views
    (BRITISH INST NON-DESTRUCTIVE TESTING, 2010) Pieringer, C.; Mery, D.
    Recently, X-rays have been adopted as the principal non-destructive testing method to identify flaws within an object that are undetectable to the naked eye. Automatic inspection using radiographic images has been made possible by incorporating image processing techniques into the process. In a previous work, we proposed a framework to detect flaws in aluminium castings using multiple views. The process consisted of flaw segmentation, matching and finally tracking the flaws along the image sequence. While the previous approach required effective segmentation and matching algorithms, this investigation focuses on a new detection approach. The proposed method combines, simultaneously, information gathered from multiple views of the scene; this does not require searching for correspondences or matching. By gathering all the projections from a 3D point, obtained from a sliding box in the 3D space, we train a classifier to learn to detect simulated flaws using all the evidence available. This paper describes our proposed method and presents its performance record in flaw detections using various classifiers. Our approach yields promising results: 94% of true positives detected with 95% sensitivity in real flaws. We conclude that simultaneously combining information from different points of view is a robust approach to flaw identification.
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    High-contrast pixels: a new feature for defect detection in X-ray testing
    (BRITISH INST NON-DESTRUCTIVE TESTING, 2006) Mery, D.
    The detection of defects in X-ray testing follows a pattern recognition scheme where feature extraction plays a very significant role. In this paper, we present a new feature based on the number of high-contrast pixels located inside a segmented potential defect related to the size of the potential defect. The developed feature can be easily computed and offers a high separability according to the Fischer linear discriminant. The feature depends on only two parameters that can be automatically determined in a training phase. The developed feature and other reported features are tested in 72 radioscopic images of aluminium wheels. The comparison shows that the separability of the developed feature is at least six times higher than the separability achieved by other features.
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    Segmentation of circular casting defects using a robust algorithm
    (BRITISH INST NON-DESTRUCTIVE TESTING, 2005) Ghoreyshi, A.; Vidal, R.; Mery, D.
    In this paper, we describe three methods for detecting defects in cast aluminium using X-radioscopic images. The first method is based on the assumption that most defects have the shape of a circular high-intensity spot. Therefore, defects are detected using a template matching-like algorithm. This method works well when the defects are far enough from the edges of the major shapes in the image, and when the image gives a closer view of the defect. The second method deals with the defects which are closer to the edges in the image, and therefore are missed by the first method. This method distinguishes between defects and edges by using the following properties of a defect: they are local maxima of the image intensity, and the distribution of the intensity in a patch around the defect should resemble more that of a corner than that of an edge. Both local maxima and corner-like properties are computed using the second order derivatives of the image intensities, and the Harris Corner Detector algorithm. The third algorithm is a simple combination of the aforementioned methods in which a pixel is considered to be a defect if it is detected as a defect by either of the two methods. We present experiments using the third method showing that 94.3% of the defects are correctly detected, with only 1.3 false alarms per image.
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    Simulation of defects in aluminium castings using CAD models of flaws and real X-ray images
    (BRITISH INST NON-DESTRUCTIVE TESTING, 2005) Mery, D.; Hahn, D.; Hitschfeld, N.
    In order to evaluate the sensitivity of defect inspection systems, it is convenient to examine simulated data. This gives the possibility to tune the parameters of the inspection method and to test the performance of the system in cases where the detection is known to be difficult. In this paper, an interactive environment for the simulation of defects in radioscopic images of aluminium castings is presented. The approach simulates only the flaws and not the whole radioscopic image of the object under test. A manifold surface is used to model a flaw with complex geometry, which is projected and superimposed onto real radioscopic images of a homogeneous object according to the exponential attenuation law for X-rays. The new grey value of a pixel, where the 3D flaw is projected, depends only on four parameters: a) the grey value of the original X-ray image without flaw; b) the linear absorption coefficient of the examined material; c) the maximal thickness observable in the radioscopic image; and d) the length of the intersection of the 3D flaw with the modelled X-ray beam, that is projected into the pixel. The approach allows the user the simulation of complex flaws at any position of an aluminium casting. Simulation results of flaws like blow holes and cracks on X-ray images are shown and contrasted with real digital images with real flaws.