Object recognition is based on complex algorithms and machine learning models, in particular deep neural networks. The process begins with pre-processing, in which the images are normalized and scaled to a uniform size to facilitate subsequent processing. This is followed by feature extraction, in which important features such as edges, textures and shapes are extracted from the image. The next phase involves modeling, often using a Convolutional Neural Network (CNN) to recognize and learn patterns in the extracted features. Finally, the trained model identifies and localizes the objects in the image, which is the actual recognition and localization.

Let’s take a look at the whole thing using an example. Let’s imagine we are in a production plant and want to identify defective components, such as screws. A camera is mounted at the edge of a production line and continuously captures images of the passing components. First, the image is captured and pre-processed. This step can include many aspects, such as adjusting the image size, brightness, focus or color profile.

In the next phase, the actual processing takes place. Relevant features are extracted from the image with the help of a Convolutional Neural Network (CNN). For example, the external shape of our screws is analyzed. The CNN is trained to recognize exact dimensions such as the width, length and thickness of the screw. If a screw in the image deviates from these specifications, the trained model identifies this defect and sends a corresponding signal.

In our example, a faulty screw that does not correspond to the expected dimensions would be directed to a separate path in the conveyor belt, while the correct screws continue on their normal path. This enables efficient quality control and ensures that only flawless components are processed further.