ViDi Suite offers significant advantages in terms of performance and simplicity for configuring and operating. It does not rely on a bar code to identify and sort products but does it based on the visual appearance of the products themselves. Its self-learning statistical engine empowers the logistic center manager to easily teach the system by providing images properly labeled in accordance with the product they show.
CHALLENGES IN LOGISTIC CENTER PRODUCT TRACKING
- Automatic product identification and classification without access to bar or matrix code
- Normal and expected changes in product or batch appearance and environmental variability exist and can cause problems. So systems that are implemented to take on this challenge need to be resilient to the mentioned variations
- Possible visual similitude of different products
- Frequent introduction of new products or packaging
Learn more at Cognex here.
In-Sight ViDi Read Tool
The In-Sight ViDi Read tool deciphers badly deformed, skewed, and poorly etched codes using optical character recognition (OCR). It works right out of the box, dramatically reducing development time, thanks to the deep learning pretrained font library. Simply define the region of interest and set the character size. In situations where new characters are introduced, without vision expertise, this robust tool can be retrained to read application-specific codes that traditional OCR tools are not able to decode.
In-Sight ViDi Check Tool
The In-Sight ViDi Check tool uses artificial intelligence to reliably detect complex features and objects and verifies parts and kits are assembled correctly based on their location within a pre-defined layout. It can be trained to create an extensive library of components, which can be located in the image even if they appear at different angles or vary in size.
In-Sight ViDi Detect Tool
The In-Sight ViDi Detect tool is ideal for finding anomalies on complex parts and surfaces, even in situations where defects can be unpredictable in their appearance. It learns from images of good parts in order to identify defective parts. This allows the tool to detect a wide range of defects that do not need to be pre-defined at the time of training.
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