Siegmund, Dirk ; Prajapati, Ashok ; Kirchbuchner, Florian ; Kuijper, Arjan (2018)
An Integrated Deep Neural Network for Defect Detection in Dynamic Textile Textures.
International Workshop on Artificial Intelligence and Pattern Recognition (IWAIPR). Havana, Cuba
doi: 10.1007/978-3-030-01132-1_9
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
This paper presents a comprehensive defect detection method for two common fabric defects groups. Most existing systems require textiles to be spread out in order to detect defects. This method can be applied when the textiles are not spread out and does not require any pre- processing. The deep learning architecture we present is based on transfer learning and localizes and recognizes cuts, holes and stain defects. Classification and localization is combined into a single system combining two different networks. The experiments this paper presents show that even without adding depth information, the network was able to distinguish between stain and shadow. This method has been successful even for textiles in voluminous shape and is less computationally intensive than other state-of-the-art methods.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Siegmund, Dirk ; Prajapati, Ashok ; Kirchbuchner, Florian ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | An Integrated Deep Neural Network for Defect Detection in Dynamic Textile Textures |
| Sprache: | Englisch |
| Publikationsjahr: | 2018 |
| Ort: | Cham |
| Verlag: | Springer |
| Buchtitel: | Progress in Artificial Intelligence and Pattern Recognition |
| Reihe: | Lecture Notes in Computer Science (LNCS) |
| Band einer Reihe: | 11047 |
| Veranstaltungstitel: | International Workshop on Artificial Intelligence and Pattern Recognition (IWAIPR) |
| Veranstaltungsort: | Havana, Cuba |
| DOI: | 10.1007/978-3-030-01132-1_9 |
| URL / URN: | https://doi.org/10.1007/978-3-030-01132-1_9 |
| Kurzbeschreibung (Abstract): | This paper presents a comprehensive defect detection method for two common fabric defects groups. Most existing systems require textiles to be spread out in order to detect defects. This method can be applied when the textiles are not spread out and does not require any pre- processing. The deep learning architecture we present is based on transfer learning and localizes and recognizes cuts, holes and stain defects. Classification and localization is combined into a single system combining two different networks. The experiments this paper presents show that even without adding depth information, the network was able to distinguish between stain and shadow. This method has been successful even for textiles in voluminous shape and is less computationally intensive than other state-of-the-art methods. |
| Freie Schlagworte: | Deep learning, Defect detection, Computer vision, Textile industry, Quality assurance |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 19 Jun 2019 11:22 |
| Letzte Änderung: | 19 Jun 2019 11:22 |
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