Deflectometry has proven to be a very precise and reliable technique for the detection and measurement of bumps, dents, waviness and scratches on specular surfaces. Phase shifted fringe patterns are successively reflected at the surface and the spatial distortion of these reflected patterns is observed with a camera to extract information about the shape of the surface. Up to now, deflectometry could not be used for diffuse reflecting surfaces, because specular reflection does not occur. With the system developed at our institute it is now possible to inspect even diffuse reflecting surfaces like unpolished metal or plastics using the deflectometric measuring
principle. Hereby the fact is exploited that a surface becomes specular when the reflected light has sufficiently large wavelength compared to the surface roughness. For the diffuse surfaces
mentioned above the adequate range of the electromagnetic spectrum is far-infrared. In our approach a reflected infrared pattern is observed with a thermal camera. By analyzing four images of the
phase shifted pattern an image is calculated, which contains information about local surface curvature. The presented method has been successfully tested for the inspection of the diffuse
surfaces of unpainted car body parts.
Specular surfaces are used in a wide variety of industrial and consumer products like varnished or chrome plated parts of car bodies, dies, molds or optical components. Shape deviations of these products usually reduce their quality regarding visual appearance and/or technical performance. One reliable method to inspect such surfaces is deflectometry. It can be employed to obtain highly accurate values representing the local curvature of the surfaces. In a deflectometric measuring system, a series of illumination patterns is reflected at the specular surface and is observed by a camera. The distortions of the patterns in the acquired images contain information about the shape of the surface. This information is suited for the detection and measurement of surface defects like bumps, dents and waviness with depths in the range of a few microns. However, without additional information about the distances between the camera and each observed surface point, a shape reconstruction is only possible in some special cases. Therefore, the reconstruction approach described in this paper uses data observed from at least two different camera positions. The data obtained is used separately to estimate the local surface curvature for each camera position. From the curvature values, the epipolar geometry for the different camera positions is recovered. Matching the curvature values along the epipolar lines yields an estimate of the 3d position of the corresponding surface points. With this additional information, the deflectometric gradient data can be integrated to represent the surface topography.
Defects of painted surfaces have proven to be visually disturbing even when their depth is only a few microns. Most inspection approaches neither enable a reliable classification of small defects nor provide a suitable human-machine interface to identify areas to be refinished. Consequently, in most cases the inspection still takes place manually and visually - an unsatisfactory compromise that lacks both objectivity and reproducibility. Our approach combines the reliability of automated methods with the acceptance and flexibility of human-based techniques. The measurement principle is based on deflectometry, and features a significantly higher sensitivity than triangulation methods. The developed system consists of a light source based on a digital micromirror device (DMD), a screen where defined patterns are projected on, as well as a mobile inspection device equipped with a head-mounted display (HMD) and a video camera. During operation, the camera captures images of different patterns reflected in the surface. By combining several images using one of the two techniques described to enhance surface defects, the resulting feature image is displayed in the HMD. This procedure takes place in real time and is repeated continuously. The system performance is demonstrated with the visual inspection of car doors. Promising results show that our prototype allows a reliable yet cost-efficient inspection of painted surfaces matching the needs of automotive industry.
This contribution presents a new fusion strategy to inspect specular surfaces. To cope with illumination problems, several images are recorded with different lighting. Typically, the information of interest is extracted from each image separately and is then combined at a decision level. However, in our approach all images are processed simultaneously by means of a centralized fusion-no matter whether the desired results are images, features or symbols. Since the information fused is closer to the source, a better exploitation of the raw data is achieved. The sensors are virtual in the sense that a single camera is employed to record all images with different illumination patterns. The fusion problem is formulated by means of an energy function. Its minimization yields the desired fusion results, which describe surface defects. The performance of the proposed methodology is illustrated by means of two case studies: the analysis of machined surfaces, and the inspection of painted free-form surfaces. The programmable light sources utilized are a DMD, and an LED based illumination device, respectively. In both cases, the results demonstrate that by generating complementary imaging situations and using fusion techniques, a reliable yet cost-efficient inspection is attained matching the needs of industry.
Specular surfaces are used in a wide variety of industrial and consumer products like varnished or chrome plated parts of car bodies, dies or molds. Defects of these parts reduce the quality regarding their visual appearance and/or their technical performance. Even defects that are only about 1 micrometer deep can lead to a rejection during quality control. Deflectometric techniques are an adequate approach to recognize and measure defects on specular surfaces, because the principle of measurement of these methods mimics the behavior of a human observer inspecting the surface. With these methods, the specular object is considered as a part of the optical system. Not the object itself but the surrounding that is reflected by the specular surface is observed in order to obtain information about the object. This technique has proven sensitive for slope and topography measurement. Inherited from the principle of measurement, especially surface parts with high curvature need a special illumination which surrounds the object under inspection to guarantee that light from any direction is reflected onto the sensor. Thus the design of a specific measurement setup requires a substantial engineering effort. To avoid the time consuming process of building, testing and redesigning the measurement setup, a system to simulate and automatically optimize the setup has been developed. Based on CAD data of the object under inspection and a model of the optical system, favorable realizations of the shape, the position and the pattern of the lighting device are determined. In addition, optimization of other system parameters, such as object position and distance relative to the camera, is performed. Finally, constraints are imposed to ascertain the feasibility of illumination system construction.
During the design and manufacturing processes of specular surfaces, waviness and shape defects may occur, reducing the quality of the surface regarding its visual appearance and/or its technical performance. Typically, these defects are only a few micrometers deep and a few centimeters wide. Beside the plain defect recognition, the reconstruction of the surface topography is of great interest, because it allows to characterize defects quantitatively. To reconstruct specular surfaces, a reflection technique based on structured-lighting is used. The specular object is considered as a part of the optical system. This technique is sensitive to changes of the slope of the surface. A series of patterns produced by an illumination system and reflected at the specular surface is observed by a camera. The distortions of the patterns in the acquired images contain information on the shape of the surface. This information is recovered through a model of the imaging function of the optical system. In contrast to previous approaches, with the proposed method it is possible to reconstruct the topography of the specular surface without an iterative approximation. This is achieved by applying a smoothness constraint to the surface data and directly calculating the surface topography from the imaging function.
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