Inspect 4.0

Automatic quality control through inspection techniques

As a production company, it has always been important to consistently produce and guarantee a high quality product. With the project Inspect 4.0, InViLab, a research group at the University of Antwerp, and Centexbel, the Belgian textile competence centre, will develop and test the very latest inspection techniques to make quality control even stronger and cheaper.


The technical textile sector plays an important role in the global textile industry; in 2018, the Belgian textile sector realized a turnover of €6.1 billion, of which €2.2 billion was realized by the technical textiles segment, with 7,600 employees. As competition from lower-wage countries increases, it becomes more and more important for Belgian textile producers and finishers to consistently guarantee a high-quality product.

machine learning

Challenges for the industry

For bare, woven textiles, inspection has historically been performed by people, watching rolls of textile pass over a light table, and manually flagging imperfections. The thickness of coated textile was typically determined via radioactive sources. As both methods present problems, (inspection by humans is time consuming and imprecise, inspection by radioactive sources is being legally phased out) a transition to automated inspection is taking place.

This typically makes use of different cameras, coupled with libraries of textile defects, to alert the user when something has gone wrong. The project Inspect 4.0 seeks to help realize this transition, by illuminating and illustrating the benefits of such systems.


The aim of Inspect 4.0 is to demonstrate how machine learning and machine vision are combined to produce flexible and accurate quality inspection systems, which can be deployed in a range of textile manufacturing setups. In Inspect 4.0 machine vision is seen as a combination of camera systems that use the wavelength spectrum of electromagnetic waves between the UV and long wave infrared spectrum, (250 nm – 140 µm) complemented with extensive data analysis.

The combination of different camera systems can be used to replace and/or enhance existing quality inspection systems, easing the transition to more automated textile inspection. The data analysis will be used for a machine learning system allowing for the detection of errors. Once the illustrative system is set up, investigations will be made into the possibility of incorporating the machine into a broader predictive maintenance framework. 

Reports & Results

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Workshop 9/9/2022

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Gunther Steenackers, InViLab "Project Overview"
Keynote: Joeri Ruyssinck, ML2Grow "AI solutions for the textile industry"
Gertrude Kignelman, Centexbel "Typical defects & textile quality inspection"
Ritchie Heirmans, InviLab "Project results after 1 year"
Ben Golsteyn, Talenco "Meltbow Line Development"

Research documents

Informative documents

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Guidance Group meeting 20-06-22
Guidance Group meeting 29-03-22
Kick-off meeting video
Inspect 4.0 Oorsprong en toekomst
Inspect 4.0 InViLab
List of participants

Project managers

Sander De Vrieze

Consultant Intellectual Property & Yarn Engineering
+32 489 447 592
+32 9 243 82 13

Gertrude Kignelman

Researcher "Textile Functionalisation & Surface Modification"
+32 9 243 82 54
+32 478 700 678 Gunther Steenackers

InViLab, University of Antwerp, Campus Groenenborger
+32 3 265 19 08

Contact Sander & Gertrude

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Project financing


TETRA Project financed by VLAIO

Tetra is a programme for practice-based research by universities of applied sciences and integrated training in universities for enterprises and non-profit organisations.

Start: 01/10/2021 - End: 30/09/2023