AI / Deep Learning
Deep learning works according to the principle of “learning”. Once the software has been trained, it can precisely assign new images to the corresponding categories. Deep learning is used in situations where conventional machine vision is not sufficient. When objects are complex and cannot be described using rules.
Classification
During classification, the entire image is assigned to a class. The classes only need to be made known to the network by means of corresponding sample images. In this method, an image is assigned a confidence value for the class, which indicates the match rate with which the presented image belongs to this class.
Object detection
With the help of object detection, various trained classes can be recognized in an image and localized in space. An object found is assigned to a class and marked with an enclosing rectangle. Recognizing and distinguishing between different objects is also used for reading fonts and optical character recognition (OCR).
Segmentation
Segmentation can be seen as a kind of refinement of object detection. Like a more accurate version of object recognition. This method assigns a class to each individual pixel of the image, including the background. This allows you to segment a found object precisely.
Anomaly detection
Anomaly detection finds deviations from a good version. The system is trained with error-free objects. The system then recognizes defects (e.g. cracks, holes, scratches) in the image as deviations. Advantage: The system learns to find errors without knowing them beforehand.
