Quality control of car upholstery

Leather defect detection in the automotive industry aims to ensure the quality and aesthetics of a vehicle’s interior finish. Leather defects can include cracks, scratches, irregularities, stains, scratches or color differences. Traditionally, detecting such defects required manual inspection of each upholstery component, which was time-consuming and subject to human error.

Vision systems for skin defect detection allow automatic detection of defects and eliminate the need for manual inspection. Such a system includes cameras and software for image analysis. The cameras are placed above the production belt on which a strip of upholstery parts passes. The cameras capture images of the leather components, and the software processes these images to detect any defects.

When defects are detected, the system automatically singles out upholstery parts with defects, allowing them to be repaired or replaced. This helps ensure a consistent quality of the vehicle interior finish, and reduces production costs and time by eliminating manual inspection.

Software that looks for errors on its own

Anomaly-finding vision algorithms are a type of artificial intelligence systems that are used to detect anomalies or deviations from standard patterns in images or video. They use machine learning to learn to recognize and classify patterns in images, and then identify those patterns that deviate from the norm.One of the most important challenges of anomaly-finding vision algorithms is ensuring that there are enough quality images and enough data to teach the algorithms. Proper training of algorithms sometimes requires updating datasets and constantly adjusting to changing conditions.

In summary, anomaly-finding vision algorithms are a powerful tool that can help detect problems and prevent failures, but their proper use requires proper data preparation and continuous learning and adjustment to new conditions.