Computer vision a key technology in digital transformation for industries

Digital Transformation
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Computer vision (CV) is a field of artificial intelligence involved in training machines to see the world like humans do by interpreting and understanding context. The main difference between a machine and a human is that an algorithm processes the information transforming it into numerical models Using digital images from cameras and processing it through algorithms, machines can accurately identify and classify objects. Although CV originated in the late fifties, it has grown exponentially in the last decade due to the digital transformation processes that are taking place today.

Computer vision has applications in many fields like health, automotive, security, industry and others. In manufacturing industry, integrated with sensors, the computer vision can offer better analysis of the industrial environment.  It combined with machine learning algorithm can improve pattern recognition. Vision-guided robots is one of the most common application used for tool and/or detail positioning on the production lines. The system identifies the precise location of an object or tool and sends these coordinates to the robot. Anomaly detection for quality control is another important application. The captured images are analyzed and compare to a pre-existing dataset in order to find anomalies in the products during the production stage. Thus, is reduced the defects in the finished products, contributing to better customer satisfaction.

Supply chain is an important area where digitization had improved the management and the quality of the services. CV has been one of the digital tools with more impact in these advances. In a complex solution artificial vision system can not only scan items from several angles and match it to the acceptance criteria but also save the accompanying metadata contributing to the quality control. Packaging inspection is another important task where CV had a great impact. A CV-based inspection solution can track whether the package had the correct length, and width, whether the edges are intact, or if a package is filled to the necessary level. Barcodes and text labels scanning also has been improve by CV. Introducing a computer vision system on a delivery site can improve the detail management process, speed up the order processing, and enhance the tracking system. Also, can detect when a product is mislabeled or misplaced, saving money and customers being not happy.

Industry 4.0 has benefited from this technology even more in time of pandemic, as it is widely used for inventory purposes. In 2018, the global market for computer vision stood at over $9,2B, and it is expected to surpass $13.0B by 2025. Both North America and Europe are headliners in the adoption of Computer Vision in manufacturing and a number of other industries Along with the growing interest in technology and market growth, companies are paying more attention to the technological advancements that AI offers. Computer Vision is one of the most widely adopted technologies. It is used by at least 20% of companies worldwide [1].

Computer Vision potential market by regions

Supported in the new potentialities brought by the advances in hardware and software digitization is changing the modern life. Modern industry is one of the fields that is experiencing the advances in digital transformation. Computer vision has benefited of the digitization process and is currently contributing the most to taking modern industry to the next level.

In future posts, we will be continue writing about technology and business trends for enterprises. Furthermore, we recommend consulting the following literature to continue your digital transformation journey:

The objective of this blog is to provide a personal vision of how digital transformation trends will be impacting in our daily activities, businesses and lifestyle.




By Alberto Villalonga twitterlinkedin2

Researcher, his research interest includes machine learning, Industry 4.0, Internet of things, and cyber-physical systems.


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One Response

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