IDENTIFICATION AND DELIMITATION
It precisely identifies and delimits areas with imperfections or defects within an image.
RAPID LEARNING
Thanks to methods based on deep neural networks, rapid learning of patterns and defect features occurs through an extensive and quick training data set. This allows the algorithm to accurately identify even the subtlest or most complex defects in images.
ANOMALY MAP
After training, the algorithm can be applied to new images to detect and isolate areas with anomalies or defects. The resulting segmentation can be visualized as a map of defective regions, facilitating inspection and detailed analysis of problematic areas.
Applications
The use of the defect segmentation algorithm is extremely advantageous in many contexts, allowing the identification of surface defects, cracks, dents, or other imperfections that could affect product quality.
MATERIAL INSPECTION
In the production of glass sheets, metals, or composite materials, the algorithm can be used to detect and isolate surface defects or imperfections, such as scratches, cracks, bubbles, or inclusions affecting material quality.
FOOD PRODUCTS
The algorithm can be employed for product inspection in industries like fruit, vegetables, or baked goods. It can identify visible defects such as spots, contaminations, or deformities on product surfaces.
ELECTRONIC COMPONENTS
In the electronics sector, the algorithm can be used for the inspection of electronic boards or components, identifying faulty connections, imperfect soldering, or visible damage on printed circuits.