Computer vision applications in simple forms have been in use for decades. However, it was not until the 2010s that with deep neural networks the ability of computers to recognize images reached human levels.
Computer vision takes the automatic control of processes to a whole other level. With the help of machine learning, systems can copy the intuition of even an experienced person, for example in quality monitoring. Systematically measured quality, on the other hand, enables accurate process optimization and rapid response to deviations.
Classification
Classification was the first application where deep neural networks were successfully used. As the name implies, classification labels an image as belonging to a category. For example, the mushroom in the picture below is classified as russula paludosa.
The deep neural net searches the image for colors and contrasts and textures and forms in the deeper layers.
For industrial use the classification is often a preprocessing stage where image is labeled as valid for further analysis. For example making sure the conveyor belt has material.
Object detection
In object detection, an area is found in the image where the desired object can be found. As an example in the figure below, the propeller has been identified and its surface area measured from the overall view.
This is particularly useful when dealing with large amounts of images and deciding if an image is worth saving for later inspection.
Segmentation
Area segmentation or semantic segmentation is the most practical tool in industrial image analysis and computer vision.
In semantic segmentation every pixel in the image is classified as belonging to one or more categories. This can be then used for example to determine the amount of good quality product, defects or background in the image. The numerical value is needed for automation, which still runs primarily on numeric values.
Try the slider below to visualize the amount of bark left.
Segmentation makes numeric production monitoring and optimization possible.
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