How machines learn to recognize patterns
Artificial intelligence (AI) has become an indispensable part of industrial image processing. Whether it’s fault detection, component classification, or optical character recognition, adaptive systems are helping to automate complex visual tasks.
But what exactly is behind terms such as “AI model” or “neural network”? And how do they differ?
An AI model is a computational system that is trained to recognize patterns in data and make decisions or predictions .
It is based on mathematical and statistical methods that make it possible to learn from examples – similar to how humans learn from experience.
AI models are inspired by the human brain: they consist of artificial neurons that are connected to each other by weights . These weights determine how much an input signal (e.g. a pixel value of an image) affects the activity of a neuron.
During the training process , the model is “fed” with many examples. It compares its predictions to the actual ground truth (e.g., “screw” or “nail”) and adjusts the weights so that the predictions become more accurate over time.
This process is known as machine learning .
Example:
An AI model can learn to distinguish from images whether a component is defect-free (“OK”) or damaged (“NOK”) – and this without having to manually program the rules.
The term “AI model” is an umbrella term for many different types of algorithms. These include, among others:
A neural network is therefore a special form of AI model that is particularly suitable for complex tasks – such as recognizing objects in images, analyzing language, or predicting nonlinear relationships.
In industrial image processing, neural networks are mainly used when classic, rule-based approaches reach their limits – for example, with irregular surfaces, variable lighting conditions or free-form objects.
An artificial neural network (ANN) is a mathematical model that is loosely based on how the human brain works.
It consists of a large number of neurons that are connected to each other in several layers called layers .
Each artificial neuron receives input signals, processes them mathematically, and passes on an output signal. The strength of this transmission is controlled by weights . Through repeated training, the network learns which features are relevant to solve a certain task.
A neural network is divided into three main layers:
The input layer takes the raw data .
In image processing, for example, these are pixel values of a camera image or extracted features such as edges, colors or contrasts.
Each input signal is passed on to the next layer.
This is where the real learning happens.
The hidden layers consist of many neurons that process the input data in several computational stages. In the process, the network recognizes increasingly abstract features:
The more hidden layers a network has, the deeper it is – hence the term deep learning.
The output layer provides the final result:
e.g. “Object is a screw”, “Error detected”, or “OK/NOK”.
The output can be a single decision or a probability distribution across multiple classes.
During training, the neural network compares its prediction with the known target output.
The difference between the two is calculated by a loss function .
The error is then distributed backwards through the mesh through a mathematical process called backpropagation to correct the weights.
This learning process is repeated many thousands of times until the model reliably recognizes patterns.
The difference between AI model and neural network lies in the degree of specialization:
| Term | Description | Example |
| AI model | Overarching term for adaptive systems that analyze data and make decisions. | Decision tree, SVM, neural network |
| Neural network | Special type of AI model that consists of many connected artificial neurons and is particularly powerful in complex tasks. | Deep Learning for Object Recognition |
Neural networks form the technological basis of many modern AI systems – including in industrial image processing. They enable smart cameras and vision sensors from wenglor to precisely detect objects, classify errors and make decisions in real time – intelligently, adaptively and future-proof.
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