Automated visual inspection

Automated visual inspection

Defect detection

“Production lines are not designed to produce defects.”

The basic method of defect detection using deep learning is anomaly detection, where the system purely classifies parts as OK or NOK, based on their deviation from the few images of an ideal part.
Apart from this method, other techniques are used in visual inspection (VI) to identify, localize and classify defects, where a substantial number of images representing such defects and features are required for training purposes. Moresoever, precise and consistent labels are key to ensuring a high performing deep learning model.

To create a balanced data set for training and validation, one needs both good images (OK) and bad images (NOK). Since ideally no components with defects are produced in manufacturing, one often finds a shortage of defect images (NOK) available for training and validation (within a reasonable time period).

Synthetic image data is the ideal solution to compensate for this bias by representing rarely occurring defects in the dataset. Synthetic data we offer is already annotated with pixel precision. Any type of defect can be represented uniquely in order to be able to train a robust model.

SI-DemoData_Car_component_close_NOK_marked
SI-DemoData-ConcreteDefects
Crack detection on concrete bridges
SI-DemoData_Car_component_close_NOK
Crack detection on aluminium parts
SI-DemoData_IronCast2_OK
Defect detection on cast iron components
SI-DemoData_IronCast2_NOK
Ageing detection on cast iron components
SI-DemoData_Car_Spritze_close_NOk_02
Defect detection on medical syringes
SI-DemoData_PCB_close_NOk_detail
Defect detection PCB components
SI-DemoData_PCB_totale_NOk_marked
Completeness check for PCBs

Haven’t found your use case?

If you are facing challenges with data acquisition and are considering the use of synthetic images for an application not listed here, contact us – we would be glad to work together towards a solution.