Siegmund, DirkDirkSiegmundPrajapati, AshokAshokPrajapatiKirchbuchner, FlorianFlorianKirchbuchnerKuijper, ArjanArjanKuijper2022-03-142022-03-142018https://publica.fraunhofer.de/handle/publica/40183110.1007/978-3-030-01132-1_9This 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.enLead Topic: Digitized WorkResearch Line: Computer vision (CV)deep learningdefect detectioncomputer visiontextile industryquality assurance006conference paper