How Machine Vision Will Change The Future

By John Oncea, Editor

Machine vision systems are transformative technologies with wide-ranging applications across multiple industries.

Machine vision exists in myriad system components, from application-specific machine vision systems to smart cameras, frame grabbers to optical sensors. It is used in applications as diverse as barcode identification and texture analysis.

It is also expected to play a more significant role in many areas of life in the future, making them more secure, efficient, and convenient. But what is machine vision and how is it being used? And how is it going to be used in the future? What components make up a machine vision system? What are the benefits of machine vision?

To answer these questions, we need to define what machine vision is. Geeks for Geeks writes, “Machine vision systems are advanced technologies designed to replicate human vision and perception in the context of automation and industrial processes. These systems integrate a combination of hardware and software components to capture and analyze visual information from the surrounding environment. The primary objective is to enable machines to make informed decisions based on the visual data they acquire.”

Control Design adds the components that make up a machine vision system “include image sensors, cameras, smart cameras, optics and lenses, light sources, computers, and software. The image itself may be full color or even might contain 3D or spectral data, but most applications are successfully executed with a standard 2D gray-scale image, which is processed more quickly and often at higher resolution with respect to cost.”

How Machine Vision Works

Put all of the above together and you’ve got a system that, in the simplest of terms, uses machines to replace human eyes for measurement and judgment. It does this by converting a captured target into an image signal through a machine vision product and then transmitting the image signal to a dedicated image processing system.

The image processing system converts the image signal to a digital signal according to pixel distribution, brightness, color, and other information that it can perform various operations to extract the characteristics of the target allowing it to control on-site equipment actions based on the discrimination results.

There are, according to Geeks for Geeks, several types of machine vision systems, each tailored for specific applications. These include:

• 2D vision systems that are often utilized for object tracking, barcode reading, and quality control.
• 3D vision systems that can quantify object dimensions while looking at intricate surfaces, made possible by a 3D vision system’s ability to store depth information.
• Color vision systems that are helpful for applications like color-based sorting and food processing quality control because they can assess the color data in images.
• Pattern recognition systems that identify and match patterns within images which are important for applications like facial recognition and character recognition.
• Motion analysis systems that track the movement of the objects or components within the process, something that is useful in robotics and automation.

Other types of machine vision are stereo vision, color vision, thermal imaging, line scan imaging, hyperspectral imaging, hyper-resolution imaging, and time-of-flight imaging, all of which enhance quality control and consistency, leading to increased productivity and efficiency by reducing the need for manual labor. They reduce the possibility of human error while at the same time capture data needed for more in-depth analysis.

These systems also improve safety in hazardous environments but do have high initial setup costs and it can be difficult to integrate them into legacy systems. Their viability also depends on the lighting conditions in which they operate and are often limited in their ability to adapt to changing environments.

The Future Of Machine Vision

Machine vision is playing a key role in the evolution of Industry 4.0, the fourth industrial revolution which is marked by the integration of advanced digital technologies into the manufacturing sector. Industry 4.0, according to EasyODM. “It encompasses concepts such as the Internet of Things (IoT), cyber-physical systems, and data analytics. Machine vision plays a crucial role in Industry 4.0 by providing real-time data capture and analysis, enabling factories to make informed decisions and improve their processes.”

Machine vision of the future will benefit from artificial intelligence (AI), deep learning, and edge computing, further enhancing its capabilities and allowing for greater accuracy, faster processing speeds, and more complex analysis.

As a result of these enhancements, machine vision will create “business opportunities and entrepreneurship, enabling the development of innovative products and services,” writes Vidya Technology. “The ability to understand and transcribe images and videos will create new possibilities to enhance sectors such as manufacturing, medicine, security, transportation, agriculture, and many others.

“Furthermore, it won’t be long until this technology is integrated into all processes of society, reaching a scenario in which AI technology facilitates the decision making of every citizen.”

More immediately, machine learning is having an impact on numerous applications, including:

• Healthcare and Medicine: Machine learning can accelerate medical research, enable early disease detection, personalize treatment plans, and lead to scientific breakthroughs. Some applications, according to TechTarget, include analyzing medical images for diagnosis, predicting disease risk, and drug discovery.
• Natural Language Processing (NLP): Advancements in NLP will improve language translation, speech recognition, text summarization, and conversational AI assistants like chatbots, according to Forbes.
• Computer Vision: Machine learning will enhance computer vision capabilities for facial recognition, autonomous vehicles, robotics, and image/video analysis.
• Cybersecurity: ML algorithms can detect vulnerabilities, analyze network traffic for anomalies indicating cyber threats, and improve threat prevention.
• Financial Services: Applications include risk modeling, fraud detection, portfolio management, and personalized financial product recommendations.
• Customer Experience: ML can enable personalized customer experiences through targeted promotions, product recommendations, and virtual assistants for customer service.
• Supply Chain and Logistics: Predictive algorithms can forecast demand, optimize inventory, automate tracking of shipments, and identify potential issues.
• Predictive Maintenance: ML models can analyze sensor data from industrial equipment to predict failures and schedule maintenance proactively.
• Personalized Education: Adaptive learning systems can customize educational content and pace based on individual student needs.

The Bright Future Of Machine Vision

By allowing machines to “see” and interpret visual data, machine vision systems improve efficiency, quality control, and decision-making in automation and industrial processes. It is crucial to understand the components, working principles, types, and functions of these systems to fully utilize their potential in various applications. As technology advances, machine vision continues to evolve and expand its capabilities, making it an essential component of modern industrial and technological processes.