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Sentiment analysis, anomaly identification, Personal Protective Equipment classification. Understand how jobs that were commonly done through human labor are now having their effectiveness increased through Computer Vision, a branch of AI that has been revolutionizing the way business is done. Image: Find out how Computer Vision has been transforming industries
Machines that see are no longer news. Computer Vision (CV), a branch of Artificial Intelligence (AI) that simulates human vision and data interpretation, has been increasingly applied to solve pain points in various industrial sectors. Examples of the technology, which can often be used in existing systems, have revolutionized the way of doing business. Learn about Artificial Intelligence and Computer Vision here. [caption id=”attachment_3221” align=”alignleft” width=”269”] Image: Find out how Computer Vision has been transforming industries
Example of iron bar counting[/caption] Through a system that captures images, which can be identical or similar to common photographic cameras, Computer Vision (CV) does not just group photographs. The big catch of the solution lies in interpreting the image data to the point of extracting necessary information, such as product counting or identification of specific objects, for example. According to Lucas Ramalho, image processing analyst at Pix Force, if a problem can be solved through human vision and interpretation, then this same problem can also be solved through technology.
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Sentiment analysis through computer vision
People's reactions to a particular product are essential for brands to understand their popularity, as well as to predict the next steps to be taken in the market. Usually gathered through textual comments, these emotions can be classified into positive, negative, and neutral. However, what if this classification could happen in person? Also known as emotion mining, sentiment analysis is one of the branches of CV that, through pre-established patterns, can interpret consumer reactions through their facial expressions. This analysis tends to be more detailed, secure, and accurate than if done through human labor, as employees can interfere with or misinterpret the feelings of others. In this example, the advantage becomes based on speed versus authenticity: while writing happens after a process of reflection, facial expression is instantaneous and reflects the genuine reaction of the brain, which unconsciously reflects actions.
Prevention of natural disasters and accidents
Another area being explored is anomaly detection, which can be exemplified through natural disasters. Here, CV systems can constantly capture images and interpret any that differ from a normal pattern - for example, mining dams in their standard state versus mining dams about to overflow due to floods.
Technology in disease detection
U-net, a Neural Network developed by Pix Force in the field of biology, is another Computer Vision (CV) solution model that has been successfully applied. Developed to operate in the biomedical imaging field at the Department of Computer Science at the University of Freiburg, Germany, the network can perform cell segmentation and detect diseases such as cancer early. There are also other segments that use CV to solve problems. Interpretation of medical exams, animal counting, robotics systems, authentication and facial recognition, volumetry calculation for raw materials, and traceability in the production chain are some examples of applications that have already become common. Counting people in environments is another example that, in fact, was widely used at the beginning of the pandemic caused by the covid-19 virus.
A solution that improves cost-benefit
It is important to understand that Computer Vision (CV) solutions are an investment that generates a series of benefits for companies. In addition to being economically effective, since solutions usually take cost-benefit into account, the technology also relieves employees from working long hours in exhausting and unsafe roles. The accuracy of CV solutions can reach up to 99%. According to an article published by the São Paulo State Research Support Foundation (Fapesp), the image processing systems market grows every year, both in the industrial and academic sectors. Taking the lead in transforming the way of doing business, Google is among the companies that invest the most in Artificial Intelligence (AI) and Computer Vision (CV). In the research universe, the California Institute of Technology (Caltech) and the Massachusetts Institute of Technology (MIT) stand out, as well as the Brazilian São Paulo State University (Unesp), Itapeva campus, and the Institute of Mathematical and Computer Sciences of the University of São Paulo (ICMC-USP), in São Carlos. The last two became references in the timber industry after creating a system capable of understanding which trees wooden boards belong to, classifying their quality. See below How much it costs to create a Computer Vision solution.
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.
