Manufacturers require accurate and timely information to improve their quality and increase the production of goods. Digital image processing can be used in this area mainly for the detection of defects.​

Quality assurance in manufacturing is demanding and expensive, but also absolutely crucial. After all, selling flawed goods results in returns and disappointed customers. Harnessing the power of image recognition and deep learning can significantly reduce the cost of visual quality control while also boosting overall process efficiency. Automating quality testing with machine learning can increase defect detection rates by up to 90%.

Step 1 - Training

The system architecture performs best at the client environment. Automatic configuration of the system is possible with examples as long as goods are produced from the same domain. It is a key feature of the model especially when defective products are rare or in case of non-reproducible anomalies.

Step 2 - Production

The system is ready to be used in production after passing a short learning phase. The system is a one-stop solution addressing multiple use cases, such as anomaly localization, defect classification, and severity assessment of defects.

Meet Our Customers

Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo
Logo