80 years of machine vision history explained: Key milestones from 1945 to 2025

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Introduction

This month, we’re digging into how it all began. The current era of “Smart Manufacturing”, encompassing machine vision and vision systems as a technology, can be traced back to just after the Second World War. In this article, we’re going to see how the field has developed over the last 80 years, ultimately leading to the current trend of “real” AI machine vision.

Machine vision is a crucial subset of computer vision, primarily applied within industrial and manufacturing environments. It enables machines to visually inspect, analyse, and make decisions based on digital images—automating what once required human eyesight and judgement. As computing capabilities and imaging technologies have evolved, so has machine vision, transitioning from a research curiosity to a critical pillar of modern automation. We’ll explore the evolution of machine vision from its conceptual birth in the mid-20th century to its rapid advancements up to the present day in 2025.

80 years of machine vision history explained: Key milestones from 1945 to 2025

Origins: Post-War Foundations (1945–1956)

The genesis of machine vision can be traced back to the broader development of artificial intelligence (AI) in the post-World War II era. The early foundations were laid not with industrial inspection in mind, but rather as part of philosophical and military inquiries into replicating human cognition and intelligence using emerging computer technologies.

Researchers began to depart from biological models like artificial neurons, instead focusing on how human thought could be more effectively emulated using modern digital computers (modern for the time!). During this phase, the concept of a machine “seeing” was not yet considered. The vision element would only emerge as AI matured.

Early AI and the Seeds of Vision (1956–1963)

A pivotal moment occurred in 1956 at the Dartmouth Conference, widely considered the birth of AI as a field. This event attracted pioneers from Carnegie Mellon, MIT, Stanford, and IBM, sparking an explosion of research.

Two major branches emerged—logical reasoning and pattern recognition. The latter would eventually evolve into machine vision. LISP, a programming language developed at MIT for AI applications, became instrumental in early image-based reasoning experiments, laying the groundwork for future vision systems.

The 1960s–1970s: First Visual Machines

Between 1963 and 1980, computer vision began taking form. At MIT, the “Blocks World” experiment marked one of the first serious efforts to enable computers to interpret real-world scenes. Cameras were used to detect blocks, and the system relayed information to a robotic arm—establishing an early link between image analysis and physical action.

David Marr, a British cognitive scientist working at MIT, became a central figure in vision theory during this era. His models of visual perception—emphasising how visual systems interpret depth, shape, and motion—remain foundational in vision science today.

The 1980s: The Birth of Machine Vision as an Industry

By the 1980s, machine vision emerged as a discrete field. The development of image processing technologies and deployment of real systems began to occur in industrial contexts.

The XCON system, developed at Carnegie Mellon for Digital Equipment Corporation, automated configuration tasks and saved the company tens of millions. Meanwhile, MIT spun out “Cognitive Experts” in 1981—one of the first commercial machine vision firms.

Key technological milestones included:
– Transition from LISP to C-based processing on standard microcomputers.
– Emergence of 8-bit greyscale image processing.
– First industrial cameras and plug-in image processing boards.
– The Windows OS revolution, starting with Windows 1.0 in 1985.

However, the industry suffered setbacks—such as the 1988 collapse of Machine Vision International—highlighting the volatility of early adoption.

The 1990s: Commercial Expansion

With the release of Windows 95 and widespread adoption of 32-bit operating systems, the 1990s saw explosive growth in machine vision.

Key trends:
– Broader adoption across electronics and semiconductor industries.
– Sharp decline in hardware costs, making machine vision accessible to smaller firms.
– Emergence of standalone “smart cameras” with onboard processing power.
– Expanding market with integration into quality control and robotics.

These advances allowed machine vision to leave the labs and integrate seamlessly into automated production lines, significantly boosting efficiency and quality assurance.

The Early 2000s: Consolidation and Interface Evolution

By the early 2000s, machine vision entered a phase of consolidation. Key developments included:
– Faster PC hardware and more user-friendly Windows interfaces.
– Migration from code-based libraries to graphical user interfaces (GUIs).
– Ergonomic designs for easy factory-floor integration.
– New digital interfaces like FireWire (IEEE 1394), allowing high-speed image transfer without frame grabbers.

The market grew steadily, supported by increasing demand for consistent quality and traceability across manufacturing sectors.

By 2004, the global machine vision market was valued at approximately $500 million, with projections of near-billion-pound potential by the following decade. However, proprietary systems, capital cost, and the need for highly skilled developers were noted as barriers to broader adoption.

The 2000s–2010s: Smart Systems and AI Re-emerge

From 2004 onward, the machine vision landscape evolved significantly with the reintroduction of AI—this time powered by vast computational advances, cheaper sensors, and big data.

Key milestones:
– Smart cameras became ubiquitous, offering embedded computing and high-resolution imaging in compact formats.
– Machine learning began to enhance image classification, particularly in pattern recognition tasks such as defect detection or surface inspection.
– The rise of GigE Vision, USB 3.0, and CoaXPress allowed faster, longer-distance data transmission between cameras and computers.
– Adoption across non-industrial sectors: medical imaging, agriculture (crop inspection), pharmaceuticals, and automotive (driver assistance systems).
– Growth of 3D vision systems and multi-camera stereo setups for robotics and logistics.

2010s–2020s: Deep Learning and Edge Computing

The last decade brought a seismic shift: deep learning revolutionised machine vision capabilities.

Highlights:
– Convolutional Neural Networks (CNNs) proved highly effective at visual classification tasks, outperforming traditional rule-based algorithms.
– Vision systems began to learn from data rather than being explicitly programmed.
– Open-source frameworks like TensorFlow and PyTorch made it easier to train and deploy custom models.
– Edge computing allowed real-time vision processing on-device, reducing latency and eliminating the need for centralised servers.
– Vision systems became integral to Industry 4.0 (and then Industry 5.0), enabling closed-loop quality control, predictive maintenance, and real-time decision-making.

2020s to 2025: The Age of Autonomous and Context-Aware Vision

In the 2020’s, machine vision is no longer just a tool—it is a strategic enabler for autonomous systems and context-aware intelligence.

Trends defining this era:
– AI-on-the-edge vision systems handle complex decision-making in real-time.
– Integration with robotic arms, drones, and AMRs (Autonomous Mobile Robots) in warehouses and factories.
– Adoption of hyperspectral and thermal imaging in areas such as agriculture, recycling, and security.
– Synthetic data and simulation environments like NVIDIA Omniverse train models without real-world images, solving the data scarcity issue.
– Continued miniaturisation and energy-efficiency improvements make machine vision viable in wearable devices and IoT sensors.
– 5G and cloud-native architectures enable distributed vision networks—real-time data sharing between cameras and AI across facilities.

Today, machine vision is embedded in almost every smart manufacturing production line, from inspecting microelectronics to identifying anomalies in real-time during automotive manufacturing. It plays a vital role in ESG compliance (e.g., reducing waste), safety (e.g., human-machine interaction monitoring), and even creative applications like art restoration and retail analytics.

2025 onwards: Modern AI machine vision

Modern machine vision AI systems can be integrated across a wide range of industrial technologies. They offer seamless compatibility with industrial cameras (both 2D and 3D), programmable logic controllers (PLCs), robotic systems, enterprise resource planning (ERP) platforms, and cloud infrastructure. This level of integration enables manufacturers to automate and manage data across every stage of production with greater efficiency and control.

A key feature of these platforms is their no-code architecture, which allows manufacturers to develop highly accurate, domain-specific vision models without the need for programming expertise. By building on robust foundational models, users can quickly adapt systems to detect, inspect, or analyse product features tailored to specific use cases or production needs. This is the promise of AI machine vision.

Flexibility is central to the design of these platforms. Manufacturers can customise entire workflows to match the demands of their operations. This includes setting up vision systems for robotic arms or fixed cameras, managing stationary or free-moving products, adjusting AI models for different product variants, configuring trigger mechanisms, and defining output responses and integrations with wider business systems.

In addition, many modern platforms include built-in tools for creating custom dashboards and analytics. These features support tasks such as root cause analysis, process optimisation, and end-to-end traceability — all essential for maintaining quality and improving decision-making on the factory floor.

By offering such adaptability and ease of use, machine vision AI platforms are playing a growing role in modernising manufacturing, helping organisations boost productivity, enhance product quality, and stay competitive in an increasingly data-driven industry.

Conclusion

Machine vision has journeyed from a theoretical curiosity within AI to an indispensable technology across modern industry. Its evolution has mirrored, and often driven, developments in computing, optics, and artificial intelligence.

From the 1940s AI labs to 2025’s edge-intelligent robotic systems, machine vision has constantly adapted to meet the growing demands of efficiency, precision, and intelligence. The next frontier may include general-purpose visual intelligence, capable of operating across dynamic, unstructured environments—ushering in truly adaptable visual machines.

As we look ahead, the convergence of quantum computing, neuromorphic processors, and further AI integration promises to keep machine vision at the forefront of technological innovation.

The future of AI-powered vision systems in medical device and pharmaceutical inspection

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Quality control comes first in the highly regulated fields of medical device and pharmaceutical manufacture. Not only is it a question of efficiency; patient safety depends on every product meeting strict industry requirements. Long the backbone of quality control in this industry, traditional inspection techniques depend on human monitoring and standard rule-based vision systems. But the limits of traditional methods are clear as production sizes rise and manufacturing techniques get more complicated. Artificial intelligence’s inclusion into machine vision systems is changing the inspection scene and allowing more precise, flexible, effective quality control.

Vision systems driven by artificial intelligence are significantly altering how producers handle production efficiency, compliance verification, and defect discovery. Unlike rule-based systems, which run on pre-programmed criteria to find flaws, artificial intelligence models—especially those using deep learning—can spot minute, hitherto undetectable variations in items. In the medical and pharmaceutical sectors, where variances can be minute but nevertheless noteworthy enough to affect product integrity, this is extremely helpful.

Detecting discrepancies in high-volume manufacturing lines is one of the important domains where artificial intelligence-powered visual systems shine. Medical equipment including syringes, catheters, and surgical tools must to be exactly perfect; even small flaws can affect their efficacy. Conventional machine vision systems must be extensively manually programmed to distinguish between good and bad products. By means of large datasets of images, artificial intelligence models can be trained to identify acceptable deviations while indicating abnormalities that might have escaped attention under a rule-based approach. This guarantees that only really faulty goods are taken off the supply chain by greatly lowering false positives and negatives.

AI-enhanced vision technologies are proving absolutely essential is pharmaceutical packaging and labelling. Regulatory compliance depends critically on batch code verification, expiration date printing, and label placement precision. Although efficient, standard OCR (optical character recognition) techniques can have difficulty with variances in font clarity, small distortions, or package material. Even in demanding environments like curved surfaces or low-contrast printing, AI models educated on thousands of packaging samples may learn to spot mistakes with significantly more dependability. These systems’ self-learning character also helps them to adjust to new materials and packaging designs without calling for significant reprogramming.

Another area where visual inspection driven by artificial intelligence is making great progress is the sterility and integrity of medical packaging. Combining human inspectors with static rule-based visual checks has long been the basis for seal validation in blister packs, vials, and ampoules. These techniques can, however, have trouble with borderline situations such minor punctures compromising sterility or somewhat uneven heat sealing. Combining artificial intelligence-based vision inspection with hyperspectral imaging or X-ray analysis will help to identify hidden flaws missed by conventional techniques, therefore preventing the release of contaminated goods into use. In the pharmaceutical industry especially, where even little packaging flaws could cause contamination hazards, this is especially important.

The capacity of AI-powered vision systems to improve traceability and compliance with strict legal criteria adds still another benefit. Manufacturers of medical devices have to follow EU MDR, ISO 13485, and FDA 21 CFR Part 820 among other international guidelines. By means of factory execution systems (MES) and enterprise resource planning (ERP), AI vision systems can interact to generate real-time inspection data, therefore guaranteeing thorough documentation of quality control methods. For recalls and regulatory checks, this degree of traceability is absolutely helpful since it lets producers monitor and separate faulty batches with until unheard-of speed and accuracy.

Visual inspection is being used to lower industrial waste and increase productivity. Conservative flaw detection thresholds of traditional inspection techniques might cause too high rejection rates. With their capacity to recognise subtle differences in product quality, artificial intelligence models can separate between actual flaws and benign deviations. Over time, this lowers needless waste without sacrificing safety criteria, therefore saving significant costs. Moreover, vision systems driven by artificial intelligence function nonstop without tiredness, enabling constant inspection quality across protracted manufacturing cycles.

Extensive data training is one of the difficulties that artificial intelligence-based visual systems in pharmaceutical and medical production presents. Unlike rule-based systems, which may be used with specified criteria, artificial intelligence models need vast amounts of high-quality photos for training. To guarantee strong model performance, this entails selecting varied datasets considering changes in illumination, angles, and material qualities. Synthetic data creation and transfer learning, however, are solving these issues and increasing the viability of artificial intelligence implementation—even for companies with limited access to big datasets.

Another factor is how well current manufacturing infrastructure integrates. Many manufacturing plants run antiquated machinery not meant to fit vision systems driven by artificial intelligence. Modern artificial intelligence solutions, on the other hand, can be used as modular updates that complement current technology to improve its capacity instead of totally replacement. Emerging as a good alternative are cloud-based artificial intelligence vision systems, which let companies use strong computer capacity without making large on-site hardware purchases.

As artificial intelligence-powered vision systems develop, their possible uses in pharmaceutical and medical production will only grow. Real-time adaptive learning will probably be included into the future generation of vision technology so that systems may dynamically change their flaw detection models as they handle fresh input. Furthermore, predictive analytics driven by artificial intelligence can help companies foresee possible quality problems before they materialise, therefore transforming quality management from a reactive to a proactive activity.

Adoption of artificial intelligence-driven visual inspection in pharmaceutical and medical industry marks a basic change in quality control strategy. AI vision systems are redefining the sector by raising accuracy, cutting waste, boosting compliance, and raising efficiency. Although data needs and execution still present difficulties, the long-term advantages much exceed the initial outlay. Manufacturers trying to satisfy the highest standards of quality and safety in an increasingly complicated regulatory environment will find that as the technology develops an invaluable tool.

How automated vision technology ensures perfect pills and tablets

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When you pick up a pill or tablet, it may appear small and modest, but have you ever realised how much precision goes into making it perfect? In the pharmaceutical industry, the quality of pills and tablets is determined by more than just their chemical content. These tiny goods must also meet demanding size, shape, weight, and appearance requirements. A single flaw or defect can jeopardise not just the product’s integrity but also consumer trust, hence quality control is a major responsibility.

Automated vision technology is critical in pharmaceutical manufacturing because it ensures precision and compliance throughout the entire process. These systems are critical in modern manufacturing, delivering innovative solutions that ensure each pill fulfils stringent industry standards and regulatory criteria. From detecting cosmetic faults to following tight MHRA, FDA, and GxP criteria, these advanced vision systems add precision, speed, and dependability to an otherwise daunting process.

Why Does Perfection Matter in Pharmaceutical Manufacturing?
In the pharmaceutical sector, flaws are more than just an aesthetic issue. A tablet with obvious flaws, such as black spots, cracks, or discolouration, may raise concerns about its quality and safety. Furthermore, any change from the specified dimensions, weight, or appearance may have an impact on the medication’s efficacy or raise regulatory concerns.

Pharmaceutical firms are expected to follow strict rules such as MHRA, GAMP, and GxP laws. Automated vision systems meet these standards by performing speedy and comprehensive inspections, decreasing human error, and ensuring quality control consistency. Manual inspection is just not possible for production lines that run at high speeds, hence automation is an integral aspect of the process.

Tablet and Pill Inspection: A Complicated Process
Pills, pills, and capsules come in all shapes and sizes. Tablets typically measure 5-12mm in diameter and 2-8mm thick, with curved tablets reaching lengths of up to 21mm. Capsules, on the other hand, are classified according to their size, which typically ranges from size 0 (biggest) to size 5. Regardless of these variances, producers must detect cosmetic flaws down to the nano level—a feat well beyond the human eye’s capabilities.

Some frequent defect categories that vision systems must recognise are:

  • Surface problems include dirt, foreign particles, scratches, cracks (lengthwise and diagonal), splits, and holes.
  • Anomalies in shape include dents, bends, double capping, and collapsed tablets.
  • Color discrepancies, including discolouration and uneven finishes.

How Does Automated Vision Technology Work?
The inspection procedure starts with a high-speed feeding mechanism. Pills are carefully transferred from hoppers or bowls into the inspection channel and positioned for best viewing by the vision system. To guarantee both sides of the pill are inspected, modern systems frequently use a two-step process:
-First Side Inspection: A vacuum dial plate holds the pill in place while cameras take photos of the top surface.
-Second Side Inspection: The pill is moved to another plate and hung securely through a vacuum while cameras analyse the bottom.
This system ensures that each tablet is properly scrutinised with no blind spots.

The Magic of Multi-Camera Vision Systems.
The true value of automated vision technology comes from its capacity to deliver a 360-degree image of each tablet or pill. Multi-camera modules, which are frequently coupled with custom optics and mirrors, enable for thorough inspection. Manufacturers may take images of all sides and angles of the pill by strategically positioning cameras and mirrors, resulting in eight distinct perspectives of a single product.

These photos are evaluated by advanced algorithms that detect even the smallest imperfections, guaranteeing that only faultless products make it into packaging. The system’s capacity to eliminate blind spots is vital for performing the high-speed, high-accuracy inspections demanded by current pharmaceutical manufacturing.

Beyond Detection: The advantages of vision systems
Automated vision systems offer distinct benefits that correspond to the pharmaceutical industry’s rigorous requirements:

  • Vision systems can inspect hundreds of pills per minute without sacrificing accuracy, resulting in optimal manufacturing line efficiency.
  • Advanced imaging can detect faults as small as a few microns, invisible to the human sight.
  • Real-time data analytics in vision systems help manufacturers spot trends, foresee concerns, and take preventive action to preserve product quality.
  • Effective Compliance: Built-in checks linked with FDA, GAMP, MHRA and GxP standards ease regulatory conformity, decreasing the risk of infractions and assuring consistent product quality.
  • Vision systems reduce waste by recognising damaged pills early in the production process, saving money and resources.

    • These specific qualities make automated vision systems indispensable in modern pharmaceutical manufacturing, where precision and compliance are critical.

Innovation on the Horizon
As technology advances, so does the possibility for automated vision systems. Emerging technologies such as artificial intelligence (AI) and machine learning are improving defect detection capabilities by allowing systems to learn from prior inspections and improve with time. Additionally, integration with Internet of Things (IoT) devices enables real-time monitoring and data analysis, giving businesses with actionable insights to further improve manufacturing operations.

Final Thoughts
Automated vision technology is transforming the pharmaceutical industry by assuring that every pill and tablet meets the highest levels of quality and safety. These technologies improve manufacturing efficiency while maintaining consumer trust by combining speed, precision, and innovation.

The next time you take a pill, remember the sophisticated processes and cutting-edge technology that secured its perfection. Automated vision inspection enables manufacturers to confidently provide clean, high-quality products, one pill at a time.

Machine vision: The key to safer, smarter medical devices

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In this month’s blog, we are discussing the highly regulated field of medical device manufacturing, where precision and compliance are not just desirable—they are mandatory. The growth of the medical devices industry means there have been substantial advances in the manufacturing of more complex devices. The complexity of these products demands high-end technology to ensure each product is of the correct quality for human use, so what’s the key to producing safer, smarter medical devices? Machine vision is one of the most important technologies that allow for the achievement of precision and compliance within the required regulations. In this article, we will touch on the aspect of machine vision in medical device manufacturing and its role in ensuring precision while serving regulatory compliance.

How Machine Vision Works in Manufacturing

Machine vision (often referred to in a factory context as industrial vision or vision systems) uses imaging techniques and software implementation to perform visual inspections on the manufacturing line for many applications where quality control inspection is required at speed. Using cameras, sensors, and complex algorithms—Visual Perception allows machines to develop a sense of sight by capturing images with the camera(s) and processing them in order to enable decision-making from visual data.

When it comes to the medical device manufacturing process, machine vision systems play a vital role in ensuring the highest levels of precision and quality. Examples include systems that can detect the smallest imperfections, measure components with sub-micron precision, and confirm every product is produced to the tightest of tolerances—all automatically.

The Significance of Medical Device Manufacturing Accuracy

Surgical instruments, injectable insulin pens, contact lenses, asthma devices and medical syringes all need high-precision manufacturing, and this is due to several reasons. The devices not only involve patient health and safety but also must be produced at a legal level, to validated specifications. Even tiny damage or a slight deviation from those specifications can lead to the collapse of these products and hence impact human life. The drive towards miniaturisation in medical devices means that more precise parts have to be manufactured than ever before. With devices getting smaller (miniaturisation) and more complex, the margin for error decreases, hence making precision even more critical.

Machine vision systems can easily maintain precision at these levels due to the precise resolution they run at. The vision systems can use advanced imaging techniques, such as 3D vision, to measure dimensions, check alignments, and verify components are manufactured within tolerance. It makes a quality-assurance check on every unit as it comes off the assembly line to prevent defects and recalls later down the line.

Meeting Compliance Regulations

The medical device industry is heavily regulated by organizations like the FDA (Food and Drug Administration) in the United States, European Medicines Agency (EMA), and other national and international bodies that set relevant standards. Regulations govern every dimension of device manufacture, from design and production to testing and distribution.

The Current Good Manufacturing Practice (CGMP) regulations issued by the FDA draw attention to quality requirements needed throughout the manufacturing process. Machine vision systems can automatically inspect components and end products according to specifications, allowing manufacturers to validate to the relevant regulations.

Machine vision systems can produce extremely stringent inspection records with the traceability required to prove compliance against established regulatory standards. This is crucial specifically for audit purposes; if needed, manufacturers must be able to show evidence that their product was manufactured according to regulations.

Using Machine Vision in the Medical Devices Production

Each of these stages using Machine Vision to ensure precision-orientation compliance with the final product. A few of the main uses are:
• Surface Inspection: Vision systems can detect scratches, cracks, and foreign material present on the surface during the manufacturing process, which may lead to a device failure in operation.
• Dimensional Measurement: Vision metrology is used for measuring component dimensions to ensure they meet design specifications.
• Assembly Verification: Machine vision ensures that components are assembled correctly, reducing defect risk from the assembly process.
• Label Verification: Understanding which label goes on which batch is essential for compliance. Machine vision systems can inspect that labels are properly positioned and the writing on them is accurate.
• Packaging Inspection: Proper packaging ensures that devices remain sterile and protected while in transit. Machine vision systems ensure that packaging is not defective and is properly sealed.
• Code Reading: Machine vision systems check and verify datamatrix & barcodes on products and packaging to maintain correct product identification throughout the supply chain.

Optimising Efficiency and Lowering Costs

The machine vision system has more benefits and is likely to improve manufacturing not only in a precise manner but also with strict compliance while increasing the efficiency of manufacturing, resulting in reduced costs. This will help speed up the throughput from these systems, where automated inspection and measurement can be done much faster and more consistently than a team of human inspectors.

Machine vision products also reduce human error, which could cost in relation to medical device manufacturing. Where a simple mistake can cause the manufacturer to recall defective products, leading them to legal lawsuits and damaging their reputation. Catching defects early can help to minimise these risks and costs, which is why machine vision offers such a huge benefit.

Manufacturers can also use inspection data to analyse trends and uncover underlying issues before they lead to defects, allowing for higher uptime with preventative maintenance of the lines.

Future Trends in Medical Device Manufacturing and Machine Vision

As technology advances, the function of machine vision systems will continue to develop in medical device manufacturing. Here are some of the likely trends we can expect to see over time.
• Integration of Artificial Intelligence (AI) with Machine Vision Systems: AI and machine learning are increasingly incorporated into machine vision systems, further advancing image analysis and pattern recognition. This will enable even greater precision and the possibility of detecting less visible imperfections that conventional vision systems might overlook.
• 3D Vision: Where 2D vision systems are traditionally used, the third dimension is being used more. Due to cross-verification, 3D vision systems provide a complete view of the components (compared to a single-plane 2D view), offering better measurement and inspection capabilities over complex shapes and assemblies.
• High-Speed Vision Systems: The push for higher-speed vision systems goes hand in hand with the increased manufacturing speeds being implemented today, and the higher speeds of machines increase demand for accurate inspection.
• Edge Computing: With the advent of edge computing, machine vision systems can process data locally with greater power and accuracy to eliminate latency for real-time decisions. This is crucial in applications that need instant feedback to change the manufacturing process.

Conclusion

Medical device manufacturing can be significantly enhanced with the incorporation of machine vision that provides superior levels of precision and compliance. For manufacturers, it also ensures compliance with rigorous regulatory needs by automating inspections and measurements with machine vision systems. While still advancing, the necessity of machine vision in this industry is only likely to become more critical as time goes on and improvements are made in efficiency, accuracy, and overall manufacturing quality.

How to boost AI machine vision systems

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Artificial Intelligence (AI) machine vision continues to develop, and using AI deep learning (DL) in automated machine vision inspection has become a valid option in those applications where clear trends, learning, and data are available for process inspection. We continue to see strong growth in the use of AI vision systems. But at what point is a decision made on which machine vision process is best for the application deployment, and when should you boost your machine vision inspection to include an element of 100% AI deep learning inspection? And how do you improve and continue to turbo-charge your AI inspection.

Let’s drill down on what we need to apply AI machine vision. To capture training data, we need a consistent setup with known samples to capture a whole series of images. High-resolution images can capture more details, which can significantly improve the accuracy of the vision system. We need images for training the AI classifier engine, images to test the classifier within the training process, and finally, a set of unseen images to confirm the trained classifier is working as it should be. Some applications are naturally more akin to AI machine vision – such as surface inspection, surface anomalies, or subtle changes to a product’s appearance.

This differs from applications that require specific data to be calculated, such as automated gauging and metrology, where particular parts need to be measured with a vision system to an exact tolerance; this is not an application suited or achievable with AI vision systems (as no such data is available from the AI classifier).

We should also be mindful of how the production systems can be deployed. As AI requires a dataset to train and work with in many cases, the system will need to be installed and images captured from the live system before a determination can be made if AI machine vision is applicable in this case. For example, in medical device and pharmaceutical applications, this is difficult, as the solution needs to be proven and validation paperwork completed before the final installation qualification, and it’s hard to validate an AI model. But in other application areas cameras can be installed and images captured as part of the installation process. Always remember the Pros and Cons of Artificial Intelligence in machine vision.

How can we boost the AI machine vision algorithm?

The first step in boosting the AI machine vision algorithm involves showing new images, more deviations from the normal, and selecting the ability to account for skew, changes in size, and overall deviation of the image. So, it’s important to make the most out of the available data by using data augmentation techniques. This includes rotating, flipping, scaling, and adding noise to the images. Data augmentation increases the diversity of the training data without needing to collect new images, which helps prevent overfitting and improves the generalisation capabilities of the AI model.

It’s important to ensure that your dataset represents the real-world scenarios your machine vision system will encounter. A balanced dataset that includes various angles, lighting conditions, and object variations is crucial for training a robust AI model. Including diverse examples helps avoid biases and makes the system more adaptable and accurate.

It’s important to ensure all elements are handled, so all versions of the reject are known and seen. The classifier will still flag those parts with deviations it has not seen before, but you can boost the AI by providing it with more data to work with. However, there is a trade-off between the time needed to retrain the classifier on a decent GPU PC and the boost you can give to the AI machine vision calculation. It’s best to utilise hardware accelerators like GPUs, TPUs, or FPGAs to speed up the training and inference of the machine vision AI models.

In general, the more data the AI has and the larger the network, the more accurate the analysis will be. Implementing a methodology for continuous integration and deployment (CI/CD) to streamline updating models in production is crucial.

Another way to boost the AI machine vision system is to combine its functionality with traditional machine vision algorithms. We do this often, knowing AI is not a panacea for all application needs. So, use an algorithm, for example, to find specific error states, and then use an AI classification to drill down on ambiguous or hard-to-spot surface/defect deviations.

Continuous Monitoring and Updating of the AI vision system

It’s extremely important to regularly monitor the performance of a machine vision system in production. We generally set up automated systems to track key performance metrics and detect any degradation in performance over time. Continuous monitoring allows timely interventions and updates to maintain the AI model’s high accuracy and reliability. Remember, the environment in which machine vision systems operate can change over time. Periodically update your models with new data to stay accurate and relevant.

Boosting AI machine vision systems involves a holistic approach that includes enhancing data quality, utilising advanced algorithms, ensuring real-time processing capabilities, and implementing robust preprocessing and postprocessing techniques. Additionally, focusing on robustness, generalisation, and continuous monitoring ensures that the system remains accurate and reliable. By following these strategies, you can significantly improve and boost the performance and applicability of your AI machine vision systems, unlocking their full potential across many industry sectors and strengthening their use in industrial automation.

Artificial Intelligence (AI) machine vision