Fabric Defect Scanning

Fabric Defect Scanning

Matrix Control

Fabric Defect Scanning

Matrix has led the way in innovating a range of fabric defect scanning solutions, establishing market recognition and adoption for this advanced technology. Many have followed in Matrix’s footsteps and created fabric defect scanners, but Matrix till this day, yields the highest accuracy alongside a strong build quality in their fabric defect scanners.

The secret sauce behind our success? You guessed it – our top-notch scanner mounting hardware. At Matrix, we’re all about keeping you, our valued customers, front and center. We’ve fine-tuned our approach to seamlessly adapt to the diverse range of machines you bring to the table.

Imagine a personalized playbook that covers all the bases. With Matrix, every twist and turn in the road becomes a part of the adventure. When faced with a new brand or machine type, we’ve got the strategy to make it work like a charm.

Our spotlight shines on the scanner mounting hardware, the unsung hero that makes the magic happen. Count on us to deliver tailored solutions that hit all the right notes.



The Future

Vision System

Matrix is in the process of creating an advanced fabric vision defect detection system that employs cutting-edge machine learning capabilities. This innovative system can autonomously detect and categorize defects present in fabrics through the synergistic integration of computer vision and machine learning methodologies. By harnessing the capabilities of artificial intelligence, this solution significantly elevates the precision and effectiveness of defect identification.

In contrast to newcomers who have introduced costly disruptive vision systems to the textile sector, Matrix is actively leveraging its extensive 66-year experience. This involves the development of a remarkably precise defect classification algorithm, alongside a rigorously evaluated Return on Investment (ROI) analysis.

Fabric defect detection approaches

Image Collection

A high-resolution video of the fabric is captured using cameras (1000 FPS+). These images contain detailed information about the fabric’s surface, including its texture, patterns, and any potential defects.


As the vision system inspects the fabric, the trained machine learning model analyzes the features & identifies potential defects. It classifies the defects based on the patterns it has learned during training.


The system generates reports that indicate the location, type, and severity of defects. These reports assist operators and quality control personnel in making informed decisions.