Towards anomaly detection: A feasibilty study for quality control in productions of nonwovens

In the face of the upcoming Corona pandemic in 2020, which continues to this day, adequate protection for all citizens has been very important and continues to be essential. An easy way to minimize the risk of infection in public, but poorly ventilated places, are FFP masks. The basic material from which FFP masks are made is nonwoven. The quality of the produced nonwoven is the basis for the correct function and thus the protection against infection. The present research work investigates methods of computer vision to detect contaminations and damages on the nonwoven. Both supervised and semi supervised methods are evaluated. For this work, an inspection system consisting of two separate acquisition systems was developed for image data acquisition. The first system is suitable for the visible wavelength and has a theoretical resolution of 2.4 μm per pixel. The second camera system is designed for the near infrared range and has a theoretical resolution of 5.5 μm per pixel. The acquisition system collects an image data set comprising 1,760 images with 920 images of defect-free nonwoven samples and 840 images of defective nonwoven samples. First, the wavelength range suitable for optical inspection of uncoated nonwoven samples is investigated. A further investigation dealt with the question of whether the reflected light or transmitted light method is more suitable for optical inspection. Finally, coated nonwoven is also inspected using the reflected light method. An investigation using the transmitted light method is not possible, as the material is almost non-transparent. Despite the small amount of data, very good results were achieved. Machine learning methods from the field of image processing are usually classified as deep learning. This means that the large network architectures require very large amounts of data in order to learn complex patterns. Publicly available datasets for method evaluation typically consist of over 1,000 images per class or defect. The nonwoven samples provided in this work and the resulting image database is about a factor of six smaller than is actually intended for the methods used. In this work it has been possible to achieve inspection accuracies of 97.5 %.