A large part of the success in the development and updating of Industry 4.0 has to do with the progressive introduction of a series of recent technologies that are increasingly finding their way into the growth plans of industrial companies. Despite the logical investment required for their implementation, their rapid development and adoption by industry allows costs to be reduced thanks to economies of scale.
Today it is impossible to think about Industry 4.0 in the near future without concepts such as machine learning, deep learning, Artificial Intelligence or artificial vision being part of the conversation. We will now describe and briefly unravel these technological concepts. Concepts that in a short period of time have gone from being unknown to already being part of today's production industry landscape.
Deep learning and artificial vision
Machine vision companies develop machine vision platforms and models that can capture, process, analyze and understand digital images. The goal of a machine vision system is none other than to generalize and realize patterns and relationships based on training data and to automatically perceive and understand visual data.
Machine learning technology is at the heart of Artificial Intelligence technology. Machine learning enables computer systems to solve very complex problems that humans cannot solve. Deep learning technology is part of machine learning. Or in other words, it is a class of machine learning algorithms.
These algorithms use a multi-layered filter system to achieve hierarchical search and retrieval of meaningful patterns. Each input layer obtains output data from a previous layer (i.e., higher level patterns are derived from a lower level). Most deep learning algorithms are artificial neural networks of various types consisting of neurons similar to those created by nature in our human brain.
Neural network in machine learning
The artificial neural network is a computational learning system that uses a network of functions to understand and translate a data input of one form into the desired output, usually another form. It is a learning system, which means that it acts not only on the basis of preset algorithms, but also by taking into account its own experience.
The neuron in deep learning is somewhat similar to a black box, which has many inputs and only one output. A neuron receives signals and forms an output signal based on them. The principles of output signal formation are governed by the internal algorithm. These algorithms are based on the model of the human brain and are designed for pattern recognition.
Neural networks interpret sensory data while labeling or grouping the raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, whether images, sound, text or time series, must be translated. Neural networks cluster and classify their data, and group unlabeled data according to similarities between sample inputs, and classify data according to training data sets. Neural networks can be considered a component of larger scale machine learning applications.
Machine vision technology is closely related to Artificial Intelligence technology, as the computer has to interpret what it sees, analyze the information it obtains through images, and act according to the algorithm.
Machine vision image processing involves actions similar to those performed by a human being when he perceives the world around him through his eyes. Thus, machine vision is a field of computer science that works to enable computers to see, identify and process images in the same way that human vision does, and then provide appropriate results.
Three basic steps in machine vision
Obtain an image
The images required for the analysis can be received by photo, video or even through 3D technology.
Image processing
Deep learning models based on various algorithms automate the process, but the models must be trained on large amounts of previously labeled or identified images. This step is called deep learning education.
Understanding the image
This step involves the interpretation of the data. Here the objects are identified, classified and grouped.
Today's machine vision platforms can be used in different ways depending on the objectives they wish to achieve.
-Facial recognition that not only recognizes human faces in the image, but also identifies the personality of the individual.
-Image segmentation that analyzes the image in pieces and analyzes each of them.
-Object detection that identifies a specific object in the image.
-Pattern detection that recognizes repeated patterns in images such as colors and shapes.
-Image classification that groups images into categories.
-Edge detection that identifies the outer edges of objects.
-Presents similarities of matching pairs in images to facilitate the classification process.
The goal of computer vision is to obtain useful results from the visual information received and processed. Based on this information, a computer can generate 2D or 3D images that can be used. For example, in the automotive industry to inform drivers and help them analyze and react to situations on the road such as traffic lights, traffic signs, pedestrians or other cars on the road. In the case of a factory warehouse, where machine vision data received and analyzed can help keep inventory on the shelves, for example.
Machine vision uses machine learning. A computer must be able to see objects, but also understand what the objects are, classifying, grouping and analyzing the data. To use deep learning solutions successfully, you must know how neural networks work and you must be able to select the type of network that best suits your objectives and be able to tune that network and select algorithms to obtain reliable and usable results.
