Authors:
Marcin Kopaczka
;
Marco Saggiomo
;
Moritz Güttler
;
Thomas Gries
and
Dorit Merhof
Affiliation:
RWTH Aachen University, Germany
Keyword(s):
Automated Visual Inspection, Industrial Image Processing, Air-jet Weaving Machine.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Pattern Recognition
Abstract:
In this paper, we present a novel approach for the fully automated detection of faulty weft threads on air-jet
weaving machines using computer vision. The proposed system consists of a camera array for image
acquisition and a classification pipeline in which we use different image processing and machine learning
methods to allow precise localization and reliable classification of defects. The camera system is introduced
and its advantages over other approaches are discussed. Subsequently, the processing steps are motivated
and described in detail, followed by an in-depth analysis of the impact of different system parameters to
allow chosing optimal algorithm combinations for the problem of faulty weft yarn detection. To analyze the
capabilities of our solution, system performance is thoroughly evaluated under realistic production settings,
showing excellent detection rates.
