This article focuses on the inspection of plastics web materials exhibiting irregular textures such as imitation wood or leather. They are produced in a continuous process at high speed. In this process, various defects occur sporadically. However, current inspection systems for plastics surfaces are able to inspect unstructured products or products with regular, i.e., highly periodic, textures, only. The proposed inspection algorithm uses the local binary pattern operator for texture feature extraction. For classification, semisupervised as well as supervised approaches are used. A simple concept for semisupervised classification is presented and applied for defect detection. The resulting defect-maps are presented to the operator. He assigns class labels that are used to train the supervised classifier in order to distinguish between different defect types. A concept for parallelization is presented allowing the efficient use of standard multicore processor PC hardware. Experiments with images of a typical product acquired in an industrial setting show a detection rate of 97% while achieving a false alarm rate below 1%. Real-time tests show that defects can be reliably detected even at haul-off speeds of 30 m/min. Further applications of the presented concept can be found in the inspection of other materials.
Today, typical polymer films consist of several functional layers, like printable surface or barrier layers. They are
produced in coextrusion processes, in which the different materials are extruded through a single die and formed to a
blown- or cast film with haul-off speeds up to 500 m/min. In the production of transparent multilayer films certain
defects, called "interfacial instabilities", can occur. They emerge from shear stress and turbulences in the material flow
during the process and result in a reduction of the mechanical properties and the optical quality of the product. Interfacial
instabilities cannot be detected by conventional film inspection systems available on the market because the optical
distortions they produce do not change the brightness of a pixel.
In this paper, an approach for solving this problem is presented. The film is illuminated with a patterned line-light source
in a backlight setting and a CCD line scan camera is used for recording the image lines. The defects can be detected
using a 1D filter tuned to the spatial-frequency of the pattern. The distortion caused by the defects leads to a local
extremum in the feature image generated by the filter, which can be easily detected by threshold segmentation.
The system has been tested in an industrial setting and proved to be fast enough for inline-inspection. Further
applications could be in the fast deflectometric inspection of high-gloss surfaces.
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