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Created to reproduce the same type of information distribution that is done by the brain's neural mechanism, Artificial Neural Networks serve to recognize patterns and relationship mechanisms existing in raw data. In addition, neural networks are capable of analyzing and grouping this data, generating intelligent responses that gradually improve. The system is a branch of Deep Learning. Image: Neural networks: what they are and how they are classified
Do you know what Neural Networks are? Artificial networks are mechanisms that mimic the natural networks of the human brain. Within a technological system, they can identify patterns and reproduce them strategically, divided so that this identification generates different types of classifications that can be implemented for specific solutions. The mechanism is capable of acquiring knowledge through experience, using a series of processing units. Artificial Neural Networks are part of Deep Learning, which is a branch of Artificial Intelligence (AI). Used to process data and identify objects through images, Deep Learning is responsible for major recent advances related to Computer Vision, and Neural Networks are part of this process. Want to understand more about Artificial Intelligence and Computer Vision? See here.
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Neural Network Classifications
Emerging in 1943, the first idea about Artificial Neural Networks was inspired by an article on how brain neurons function. Since then, and through the use of AI, systems have been improved to reach the solutions we have today. First, it is important to understand how networks are divided into Supervised Neural Networks and Unsupervised Neural Networks. When a neural network is classified as Unsupervised, it does not use feedback information to make modifications to the values of synaptic connections. This means there is no answer considered right or wrong - in this case, the network identifies what most closely resembles the pattern used. The unsupervised system is used when it is not possible to identify what is being searched for. Here, it identifies features common to images, such as repeated appearances of a certain element or color. As a final response, it is possible to understand whether or not the figures have the element in question. On the other hand, Supervised Neural Networks work in a different way. Technician Lucas Ramalho, image processing analyst at Pix Force, describes the operation of the segment by comparing it to a black box, in which various types of information are inserted, according to the expected response. "It's like putting a series of data we want into the black box so it can learn a pattern, and then at the output it can return to us what it identified," Lucas reinforces.
Application areas of neural networks
With the growing application of satellites that capture information from space through images, Artificial Neural Networks have been increasingly used. To define their main areas of application, it is possible to separate them into networks with classification, detection, and segmentation mechanisms. In this way, it is easy to understand how they collaborate on the existence of projects, which can encompass security, education, data recognition, and much more. In classification systems, images are separated into possible categories. Through it, one knows if the image contains a certain element, such as a cat. In the same way, it is possible to identify if the same images show a cat, a hare, or a dog. The detection system is one of the most widely used today, as it can interpret data through pre-existing formulas. This means that there is no need for new programming every time one wants to find an image pattern. Here it is possible to identify a pattern through several elements that can build it, an example being the identification of people or objects in a given space. Finally, the segmentation mechanism generally becomes a combination of classification and detection systems. With it, a pattern is identified through everything that exists around the pixels that make up each item of an image, using a mask. In other words, it becomes possible to differentiate one element from another.
The Yolo detector and its application examples
Image: Neural networks: what they are and how they are classified
Yolo is considered one of the most applicable systems for image identification within Computer Vision. Working through detection, it can interpret a pattern and identify images and locations regarding it. Because of this, the detector is very useful in common industry segments. Applied to security cameras that may already be in use, Yolo can signal whether employees are using all Personal Protective Equipment (PPE), such as overalls, gloves, helmets, and hearing protectors. In the same way, it is possible to perceive if there are people in risk areas.
Artificial Neural Networks and Computer Vision
It is important to realize how much the aspects of Computer Vision contribute to large-scale processes being carried out quickly and safely, bringing efficiency to methods that were previously done manually. In addition to providing ways for companies to say goodbye to bureaucratic and low-accuracy solutions, the technology works in a mechanical and continuous manner, freeing employees from dangerous and tiring roles. Applying solutions with disruptive technologies in corporations is an effective way to work towards exponential growth, as mechanical processes are no longer at the mercy of human error, and employees can be distributed into strategic roles. Want to keep learning? Discover the origin and the future of Computer Vision .
Fabio Caraça
Fábio Caraça is the Chief Growth Officer at Pix Force. He leads Pix Force's transformation into a scalable SaaS operation, combining strategic vision, culture, and high-impact execution.
