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Inspection of presence and color using industrial vision

Inspection of presence and color using industrial vision.

The reliability of the inspection for the presence/absence of essential components crucial for the integrity of final products, such as bottle caps or car wheel studs, is of paramount importance concerning customer safety. The same applies to the verification of expiration and use-by dates on perishable products.

Example of appearance inspection: Detection of foreign bodies, scratches, and dirt.

In many cases, color is the sole means of differentiating objects. Therefore, the analysis and recognition of colors play a crucial role in detecting the presence/absence of a component. Our vision tools are equipped with precise capabilities to recognize colors, interpret variations, and distinguish shades within the same chromatic range.

Industrial application examples


During the inspection of automotive wheels, often made of reflective metal, it can be challenging to detect the presence of studs, also known as wheel bolts. Vision solutions combined with Deep Learning can accurately detect the presence/absence of studs despite the reflective nature of the wheels and the design of the bolts.


In the consumer goods industry, color is, in most cases, the distinctive characteristic that allows for the inspection of the presence/absence of components. This is evident in products like blush palettes, representing a complex case of color control for conventional industrial vision. To distinguish products and identify the absence or presence of a color in a palette, it is necessary to be able to identify a combination of colors as a single color. This is where color extraction and segmentation tools in industrial vision come into play, capable of ultra-fast extraction of colors, even in visually complex environments.


In the vaccine industry, developers and laboratory technicians require reliable and powerful tools to identify the presence of vials. The industrial vision systems and error control tools that we offer are easy to program and capable of quickly detecting the presence or absence of vials.


CIRA vision systems conduct pass/fail inspections and trigger a rejection when a defective product or packaging is detected. In-Sight vision sensors count the objects inside a package, which passes or fails the inspection based on the programmed value. They verify the presence of all bottles or products, even when packaged under shrink film, enabling food product manufacturers to control errors in their operations and maintain high customer satisfaction.

Cognex's Deep Learning-based part location tool identifies features and complex objects by learning from annotated images. Intelligent algorithms locate different types of products in highly cluttered backgrounds or other complex bulk objects. To train the tool, the user provides images where the targeted features are marked.

In short, our vision systems reliably and accurately detect the presence/absence of components in complex visual environments, surpassing the limitations of conventional industrial vision.