Browsing by Author "Mery, D."
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- ItemA 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.
- ItemActive 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%.
- ItemFlaw 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.
- ItemHigh-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.