Computer vision is a branch of artificial intelligence that focuses on replicating the ability of humans to perceive and understand the world visually. Through the use of advanced algorithms and sensors, machine vision has made significant advances in several areas of industry. In this blog, we will explore the current uses of machine vision, the types of sensors used, potential new applications, its relationship with artificial intelligence and a future estimate of its evolution. Translated with www.DeepL.com/Translator (free version)
USES
Machine vision has become an invaluable tool in various industrial sectors, revolutionizing the way many tasks are carried out. Here are some outstanding examples of its applications:
Process automation: Machine vision can automate tasks that previously required human intervention. For example, in the manufacturing industry, vision systems can automatically inspect products for defects or manufacturing errors, improving process quality and efficiency.
Robotics: Robots equipped with vision systems can recognize and manipulate objects in changing environments. This is especially useful in logistics and the supply chain, where robots can accurately and efficiently sort and pack products.
Security and surveillance: Machine vision plays a key role in security and surveillance, whether in public or private environments. Video surveillance systems can analyze images in real time to detect suspicious activity or identify specific objects, improving incident response and prevention capabilities.
Medicine and health: Machine vision has found applications in medicine, from diagnostic imaging to robot-assisted surgery. Vision algorithms can analyze medical images to identify abnormalities or help in planning more precise treatments.
Types of sensors
Machine vision requires the use of sensors capable of capturing visual information for further processing. Some of the most commonly used sensors are:
CCD and CMOS cameras: These sensors convert light into digital signals that can be processed by vision algorithms. CMOS cameras are especially popular because of their low power consumption and their ability to capture images at high speed.
LIDAR: LIDAR uses laser pulses to measure distances and create high-resolution three-dimensional maps. It is widely used in autonomous vehicles for obstacle recognition and precise navigation.
Thermal sensors: These sensors capture thermal radiation emitted by objects and are used in applications such as fire detection, temperature monitoring and night vision.
Multispectral cameras: These cameras capture images at different wavelengths, allowing the detection of features that are not visible to the human eye. They are used in agriculture for crop monitoring and disease detection.
New applications
New applications and future developments
Machine vision continues to evolve rapidly and is expected to continue to expand its applications in the future. Some of the areas where significant growth is expected are:
Augmented and virtual reality: Machine vision plays a key role in creating immersive experiences in virtual environments and in superimposing digital information on the real world, enabling new ways of interacting with the environment.
Smart cities: Machine vision can contribute to the development of more efficient and safer cities. For example, real-time image analysis can help manage traffic, detect violations and improve waste management.
Healthcare: Machine vision is expected to have a significant impact on medical care, from more accurate diagnoses to safer and more efficient surgeries. In addition, it could be used to monitor patients and detect early signs of disease.
Relationship with artificial intelligence
Machine vision is closely related to artificial intelligence. Machine learning algorithms and neural networks are fundamental to image processing and analysis. Artificial intelligence allows vision systems to learn and improve over time, adapting to new scenarios and improving their accuracy.
Evolution
Machine vision has a promising future and is expected to continue to advance as new technologies are developed. Some of the future trends and advancements include:
Improved detection accuracy and speed: Vision algorithms will become more accurate and faster, enabling more reliable detection of objects and features in real time.
Integration with other technologies: Machine vision will increasingly integrate with other emerging technologies, such as robotics, augmented reality and the Internet of Things, expanding its capabilities and applications.
Advances in context understanding: Vision systems will be able to better understand the context they are in, enabling them to make smarter, more adaptive decisions.
