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Image acquisition, evaluation and segmentation of thermal cutting edges using a mobile device

: Mitri, Omar de; Stahl, Janek; Jauch, Christian; Distante, Cosimo

Volltext urn:nbn:de:0011-n-5526668 (4.6 MByte PDF)
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Copyright Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 14.8.2019

Stella, Ettore ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.; European Optical Society -EOS-:
Multimodal Sensing: Technologies and Applications : 24-27 June 2019, Munich, Germany
Bellingham, WA: SPIE, 2019 (Proceedings of SPIE 11059)
Paper 110590U, 10 S.
Optical Metrology Conference <2019, Munich>
Conference "Multimodal Sensing - Technologies and Applications" <2019, Munich>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()
Bildqualität; Convolutional Neural Network; Maschinelles Lernen; Segmentierung (Bildverarbeitung); Semantik

In sheet metal production the quality of a cut determines the conditions for a possible postprocessing. Considering the roughness as a parameter for assessing the quality of the cut edge, different techniques have been developed that use texture analysis and convolutional neural networks. All methods available require the use of appropriate equipment and work only in fixed light conditions. In order to discover new applications in the contexts of Industry 4.0, there is a necessity to go beyond their intrinsic limits as camera types and light condition while ensuring the same level of performance. Taking into account the strong increase of the smartphones features in recent years and the fact that their performance in some respect is now comparable to that of a PC with a middle-range mirrorless camera, it is no longer utopian to think of a new out-of-the-box use of these devices that employs the capability in a new way and in a new context. Therefore, we present a method that uses a mobile device with a camera to guarantee images of sufficient quality that can be used for further processing in order to determine the quality of the metal sheet edge. After the image acquisition of the sheet metal edge in real condition of use, the method uses a trained deep neural network to identify the sheet metal edge present in the picture. After the segmentation a no-reference image quality algorithm provides an image quality index, in terms of blurriness, for the image region of the cut edge. This way it is possible for the further evaluation of the cut edge to only consider image data that satisfies a specific quality, ignoring all the parts of the picture with a bad image quality.