Construction of pattern recognition system optimized for X-ray inspection of plastic electronics and OLED displays (original) (raw)
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Computer Vision Technology for X-ray Testing
X-ray testing has been developed for inspection of materials or objects, where the aim is to analyze -nondestructively-those inner parts that are undetectable to the naked eye. Thus, Xray testing is used to determine if a test object deviates from a given set of specifications. Typical applications are inspection of automotive parts, quality control of welds, screening of baggage, analysis of food products and inspection of cargos. In order to achieve efficient and effective X-ray testing, automated and semi-automated systems based on computer vision algorithms are being developed to execute this task. In this paper, we present a general overview of computer vision approaches that have been used in X-ray testing. In addition, we review some techniques that have been applied in certain relevant applications; and we introduce a public database of X-ray images that can be used for testing and evaluation of image analysis and computer vision algorithms. Finally, we conclude that there are some areas -like casting inspection where automated systems are very effective, and other application areas -such as baggage screening-where human inspection is still used; there are certain application areas -like weld and cargo inspections-where the process is semiautomatic; and there is some research in areas -including food analysis-where processes are beginning to be characterized by the use of X-ray imaging.
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Radiographic inspection is a reliable non-destructive test, widely used for integrity evaluation of structures and equipments. Nowadays, high quality images with very accurate resolutions have been supported by modern digital radiographic systems. However, the image analysis for internal defect detection and geometric characterization is still a not totally automated task. The main reason is that image analysis is usually a very complex task, which involves heuristic decisions based on experiences, as object detection and recognition. For that reason, a new automatic radiographic image analysis system was developed in order to identify important components or component parts, which must be inspected separately, as weld joints, pipe walls, pipe wall thicknesses, valves and mechanical parts. The developed methodology involves the use of a genetic algorithm search to find desirable patterns on the image. Image indexing procedures are used for a final verification process. As a result, the system offers quick and correct answers and also flexibility to be applied in others applications.
2007
Casting is one of the most important processes in the manufacture of parts for various kinds of industries, among which the automotive industry stands out. Like every manufacturing process, there is the possibility of the occurrence of defects in the materials from which the parts are made, as well as of the appearance of faults during their operation. One of the most important tools for verifying the integrity of cast parts is radioscopy. This paper presents pattern recognition methodologies in radioscopic images of cast automotive parts for the detection of defects. Image processing techniques were applied to extract features to be used as input of the pattern classifiers developed by artificial neural networks. To estimate the accuracy of the classifiers, use was made of random selection techniques with sample reposition (Bootstrap technique) and without sample reposition. This work can be considered innovative in that field of research, and the results obtained motivate this paper.
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A new approach for defects detection in low contrast digital images (X-ray or photos) and images with uneven background illumination is presented in this paper. The algorithm comprises two main stages: image pre-processing (noise suppression and correction of the uneven illumination) and adaptive defects segmentation. This approach permits the successful detection of different kinds of defects and irregularities and ensures high accuracy. It is suitable for the analysis of X-ray images of welds, or photos of pipes, plates, etc. The experimental results obtained with the software implementation of the described algorithms prove their efficiency. The paper also points up the advantages of the presented algorithm in comparison with some well-known methods for nondestructive control.
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Adaptive Reference Image Set Selection in Automated X-Ray Inspection
The automatic radioscopic inspection of industrial parts usually uses reference based methods. These methods select, as benchmark for comparison, image data from good parts to detect the anomalies of parts under inspection. However, parts can vary within the specification during the production process, which makes comparison of older reference image sets with current images of the parts difficult and increases the probability of false rejections. To counter this variability, the reference image sets have to be updated. This paper processes an adaptive reference image set selection procedure to be used in the assisted defect recognition (ADR) system in turbine blade inspection. The procedure first selects and initial reference image set using and approach called ADR Model Optimizer and then uses positive rate in a sliding-time window to determine the need to update the reference image set. Whenever there is a need, the ADR Model Optimizer is retrained with new consisting of the old reference image sets augmented with false rejected images to generate a new reference image set. The experimental result demonstrates that the proposed procedure can adaptively select a reference image set, leading to an inspection process with a high true positive rate and a low false positive rate.
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This paper describes how to classify a data set containing features extracted from metal strips, using pattern recognition algorithms. In the first part, a short resume of pattern recognition principles and algorithms is presented, while in the second part the techniques are applied on steel samples obtained from the Anshan Steel Corporation, P.R. China. From the images made and pre-processed by the Institute of Bildsame Formgebung Aachen, Germany, features were extracted using ParsyVision from Parsytec GmbH Aachen, Germany. On these features we used several classifiers. The influence of the feature set size and sample size of the master set of samples was illuminated. Finally we established a checklist for pilot projects on automatic steel inspection systems.
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Non-destructive testing (NDT) is used in the industry to check for the properties of the material, internal flaws, etc. without cutting open the samples. Digital Radiography (DR), an NDT, is a form of x-ray imaging, where digital x-ray sensors are used. Advantages of DR include time efficiency, ability to digitally enhance and transfer images. In the proposed system, solid industrial objects like computer chip, reactor parts, etc. are considered. The proposed software converts these radiographs into tomographic images (virtual slices) as done in Computed Tomography (CT). CT is another powerful Non-Destructive Evaluation technique for producing 2D and 3D cross-sectional images of an object from the x-ray images. CT is widely used in the medical and in the industrial sectors. As the slices of the object can be viewed using the software, internal flaws, defects and the overall product can be observed. 3-D models of sample objects are reconstructed from the set of x-ray frames.